Tracking Using Continuous Shape Model Learning in the Presence of Occlusion

This paper presents a Bayesian framework for a new model-based learning method, which is able to track nonrigid objects in the presence of occlusions, based on a dynamic shape description in terms of a set of corners. Tracking is done by estimating the new position of the target in a multimodal voting space. However, occlusion events and clutter may affect the model learning, leading to a distraction in the estimation of the new position of the target as well as incorrect updating of the shape model. This method takes advantage of automatic decisions regarding how to learn the model in different environments, by estimating the possible presence of distracters and regulating corner updating on the basis of these estimations. Moreover, by introducing the corner feature vector classification, the method is able to continue learning the model dynamically, even in such situations. Experimental results show a successful tracking along with a more precise estimation of shape and motion during occlusion events

A fast cardiac electromechanics model coupling the Eikonal and the nonlinear mechanics equations

We present a new model of human cardiac electromechanics for the left ventricle where electrophysiology is described by a Reaction-Eikonal model and which enables an off-line resolution of the reaction model, thus entailing a big saving of computational time. Subcellular dynamics is coupled with a model of tissue mechanics, which is in turn coupled with a Windkessel model for blood circulation. Our numerical results show that the proposed model is able to provide a physiological response to changes in certain variables (end-diastolic volume, total peripheral resistance, contractility). We also show that our model is able to reproduce with high accuracy and with a considerably lower computational time the results that we would obtain if the monodomain model should be used in place of the Eikonal model

Incremental learning of abnormalities in autonomous systems

In autonomous systems, self-awareness capabilities are useful to allow artificial agents to detect abnormal situations based on previous experiences. This paper presents a method that facilitates the incremental learning of new models by an agent. Available learned models can dynamically generate probabilistic predictions as well as evaluate their mismatch from current observations. Observed mismatches are grouped through an unsupervised learning strategy into different classes, each of them corresponding to a dynamic model in a given region of the state space. Such clusters define switching Dynamic Bayesian Networks (DBNs) employed for predicting future instances and detect anomalies. Inferences generated by several DBNs that use different sensorial data are compared quantitatively. For testing the proposed approach, it is considered the multi-sensorial data generated by a robot performing various tasks in a controlled environment and a real autonomous vehicle moving at a University Campus

A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach

This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference (AIn), and a cognitive-UAV is employed as a case study. An Active Generalized Dynamic Bayesian Network (Active-GDBN) is proposed to represent the external environment that jointly encodes the physical signal dynamics and the dynamic interaction between UAV and jammer in the spectrum. We cast the action and planning as a Bayesian inference problem that can be solved by avoiding surprising states (minimizing abnormality) during online learning. Simulation results verify the effectiveness of the proposed AIn approach in minimizing abnormalities (maximizing rewards) and has a high convergence speed by comparing it with the conventional Frequency Hopping and Q-learnin

TaintHLS: High-Level Synthesis For Dynamic Information Flow Tracking

Dynamic Information Flow Tracking (DIFT) is a technique to track potential security vulnerabilities in software and hardware systems at run time. Untrusted data are marked with tags (tainted), which are propagated through the system and their potential for unsafe use is analyzed to prevent them. DIFT is not supported in heterogeneous systems especially hardware accelerators. Currently, DIFT is manually generated and integrated into the accelerators. This process is error-prone, potentially hurting the process of identifying security violations in heterogeneous systems. We present TAINTHLS, to automatically generate a micro-architecture to support baseline operations and a shadow microarchitecture for intrinsic DIFT support in hardware accelerators while providing variable granularity of taint tags. TaintHLS offers a companion high-level synthesis (HLS) methodology to automatically generate such DIFT-enabled accelerators from a high-level specification. We extended a state-of-the-art HLS tool to generate DIFT-enhanced accelerators and demonstrated the approach on numerous benchmarks. The DIFT-enabled accelerators have negligible performance and no more than 30% hardware overhead

Mortality of Patients with Hematological Malignancy after Admission to the Intensive Care Unit

Background: The admission of patients with malignancies to an intensive care unit (ICU) still remains a matter of substantial controversy. The identification of factors that potentially influence the patient outcome can help ICU professionals make appropriate decisions. Patients and Methods: 90 adult patients with hematological malignancy (leukemia 47.8%, high-grade lymphoma 50%) admitted to the ICU were analyzed retrospectively in this single-center study considering numerous variables with regard to their influence on ICU and day-100 mortality. Results: The median simplified acute physiology score (SAPS) II at ICU admission was 55 (ICU survivors 47 vs. 60.5 for non-survivors). The overall ICU mortality rate was 45.6%. With multivariate regression analysis, patients admitted with sepsis and acute respiratory failure had a significantly increased ICU mortality (sepsis odds ratio (OR) 9.12, 95% confidence interval (CI) 1.1-99.7, p = 0.04; respiratory failure OR 13.72, 95% CI 1.39-136.15, p = 0.025). Additional factors associated with an increased mortality were: high doses of catecholamines (ICU: OR 7.37, p = 0.005; day 100: hazard ratio (HR) 2.96, p < 0.0001), renal replacement therapy (day 100: HR 1.93, p = 0.026), and high SAPS II (ICU: HR 1.05, p = 0.038; day 100: HR 1.2, p = 0.027). Conclusion: The decision for or against ICU admission of patients with hematological diseases should become increasingly independent of the underlying malignant disease

Phenolic profile and biological activity of table grapes (Vitis vinifera L.)

Table grapes are largely produced and consumed in the Mediterranean area. Furthermore, in the last years, the commercial interest in table grapes and other non-alcoholic grape products has notably increased worldwide. In addition to the nutritional aspects, polyphenol compounds in fresh grapes could exert positive effects on human health. The aim of this study was the characterization of the phenolic pattern of sixteen grape varieties and the evaluation of the associated antioxidant capacity and anti-inflammatory activity at gastric level. The methods used were: 1) Folin-Cocalteau\u2019s assay for the quantification of total polyphenol content; 2) High-Performance Liquid Chromatography (HPLC) coupled with Diode-Array Detector (DAD) to perform the quantitative analysis of grapes phenolic fraction 3) DPPH (1,1,-diphenil-2-picrylhydrazyl) spectrophotometric assay for the assessment of radical scavenging activity; 4) assessment of IL-8 release from human gastric epithelial cells to evaluate the anti-inflammatory activity of grape extracts. Some grapes, including seedless varieties, showed that the phenolic pattern was highly correlated with the biological activities, and, in particular, with peel and seeds portion. These results suggest that selected grape varieties could represent, also for consumers who do not drink wine, a source of healthy compounds potentially able to counteract oxidative stress and gastric inflammation