scholarly journals Layer entanglement in multiplex, temporal multiplex, and coupled multilayer networks

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Blaž Škrlj ◽  
Benjamin Renoust
Keyword(s):  
Real World ◽  
Biological Networks ◽  
Real Life ◽  
Network Activity ◽  
Dependent Data ◽  
World Systems ◽  
Multilayer Networks ◽  
Single Model ◽  
Elementary Layer ◽  
Random Generator

Abstract Complex networks, such as transportation networks, social networks, or biological networks, capture the complex system they model by often representing only one type of interactions. In real world systems, there may be many different aspects that connect entities together. These can be captured using multilayer networks, which combine different modalities of interactions in a single model. Coupling in multilayer networks may exhibit different properties which can be related to the very nature of the data they model (or to events in time-dependent data). We hypothesise that such properties may be reflected in the way layers are intertwined. In this paper, we investigated these through the prism of layer entanglement in coupled multilayer networks. We test over 30 real-life networks in 6 different disciplines (social, genetic, transport, co-authorship, trade, and neuronal networks). We further propose a random generator, displaying comparable patterns of elementary layer entanglement and transition coupling entanglement across 1,329,696 synthetic coupled multilayer networks. Our experiments demonstrate difference of layer entanglement across disciplines, and even suggest a link between entanglement intensity and homophily. We additionally study entanglement in 3 real world temporal datasets displaying a potential rise in entanglement activity prior to other network activity.

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

Phenomenological Modelling

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Real World ◽  
Biological Diversity ◽  
False Positives ◽  
World Systems ◽  
Economic Activities ◽  
Essential Services ◽  
Low Dimension ◽  
Convergent Cross Mapping

Detecting causal interactions among climatic, environmental, and human forces in complex biophysical systems is essential for understanding how these systems function and how public policies can be devised that protect the flow of essential services to biological diversity, agriculture, and other core economic activities. Convergent Cross Mapping (CCM) detects causal networks in real-world systems diagnosed with deterministic, low-dimension, and nonlinear dynamics. If CCM detects correspondence between phase spaces reconstructed from observed time series variables, then the variables are determined to causally interact in the same dynamic system. CCM can give false positives by misconstruing synchronized variables as causally interactive. Extended (delayed) CCM screens for false positives among synchronized variables.

scholarly journals Higher-order temporal network effects through triplet evolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Higher Order ◽  
Prediction Algorithm ◽  
Interaction Patterns ◽  
Temporal Networks ◽  
World Systems ◽  
Order Interaction ◽  
Space And Time ◽  
Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

scholarly journals Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

2021 ◽  
Author(s):  
Amarildo Likmeta ◽  
Alberto Maria Metelli ◽  
Giorgia Ramponi ◽  
Andrea Tirinzoni ◽  
Matteo Giuliani ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Real Life ◽  
User Preferences ◽  
Inverse Reinforcement Learning ◽  
Water Release ◽  
Reward Function ◽  
Model Free ◽  
Conflicting Objectives ◽  
Multiple Experts

AbstractIn real-world applications, inferring the intentions of expert agents (e.g., human operators) can be fundamental to understand how possibly conflicting objectives are managed, helping to interpret the demonstrated behavior. In this paper, we discuss how inverse reinforcement learning (IRL) can be employed to retrieve the reward function implicitly optimized by expert agents acting in real applications. Scaling IRL to real-world cases has proved challenging as typically only a fixed dataset of demonstrations is available and further interactions with the environment are not allowed. For this reason, we resort to a class of truly batch model-free IRL algorithms and we present three application scenarios: (1) the high-level decision-making problem in the highway driving scenario, and (2) inferring the user preferences in a social network (Twitter), and (3) the management of the water release in the Como Lake. For each of these scenarios, we provide formalization, experiments and a discussion to interpret the obtained results.

scholarly journals Asymmetry underlies stability in power grids

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ferenc Molnar ◽  
Takashi Nishikawa ◽  
Adilson E. Motter
Keyword(s):  
Symmetry Breaking ◽  
Real World ◽  
Power Grids ◽  
Stable Operation ◽  
World Systems ◽  
Symmetric State ◽  
Network Systems ◽  
Additional Improvement ◽  
The Stability ◽  
General Method

AbstractBehavioral homogeneity is often critical for the functioning of network systems of interacting entities. In power grids, whose stable operation requires generator frequencies to be synchronized—and thus homogeneous—across the network, previous work suggests that the stability of synchronous states can be improved by making the generators homogeneous. Here, we show that a substantial additional improvement is possible by instead making the generators suitably heterogeneous. We develop a general method for attributing this counterintuitive effect to converse symmetry breaking, a recently established phenomenon in which the system must be asymmetric to maintain a stable symmetric state. These findings constitute the first demonstration of converse symmetry breaking in real-world systems, and our method promises to enable identification of this phenomenon in other networks whose functions rely on behavioral homogeneity.

scholarly journals Improving the performance of a radio-frequency localization system in adverse outdoor applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Marcelo N. de Sousa ◽  
Ricardo Sant’Ana ◽  
Rigel P. Fernandes ◽  
Julio Cesar Duarte ◽  
José A. Apolinário ◽  
...  
Keyword(s):  
Random Forest ◽  
Ray Tracing ◽  
Real World ◽  
Practical Implication ◽  
Real Life ◽  
Simulated Data ◽  
Real Data ◽  
Gradient Boosting ◽  
Real World Data ◽  
Localization Accuracy

AbstractIn outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

scholarly journals An Adaptive Game-Based Learning Strategy for Children Road Safety Education and Practice in Virtual Space

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3661
Author(s):  
Noman Khan ◽  
Khan Muhammad ◽  
Tanveer Hussain ◽  
Mansoor Nasir ◽  
Muhammad Munsif ◽  
...  
Keyword(s):  
Real World ◽  
Road Safety ◽  
Learning Strategy ◽  
Real Life ◽  
Safety Training ◽  
Virtual Space ◽  
Safety Education ◽  
Average Output ◽  
Training Environment ◽  
The Road

Virtual reality (VR) has been widely used as a tool to assist people by letting them learn and simulate situations that are too dangerous and risky to practice in real life, and one of these is road safety training for children. Traditional video- and presentation-based road safety training has average output results as it lacks physical practice and the involvement of children during training, without any practical testing examination to check the learned abilities of a child before their exposure to real-world environments. Therefore, in this paper, we propose a 3D realistic open-ended VR and Kinect sensor-based training setup using the Unity game engine, wherein children are educated and involved in road safety exercises. The proposed system applies the concepts of VR in a game-like setting to let the children learn about traffic rules and practice them in their homes without any risk of being exposed to the outside environment. Thus, with our interactive and immersive training environment, we aim to minimize road accidents involving children and contribute to the generic domain of healthcare. Furthermore, the proposed framework evaluates the overall performance of the students in a virtual environment (VE) to develop their road-awareness skills. To ensure safety, the proposed system has an extra examination layer for children’s abilities evaluation, whereby a child is considered fit for real-world practice in cases where they fulfil certain criteria by achieving set scores. To show the robustness and stability of the proposed system, we conduct four types of subjective activities by involving a group of ten students with average grades in their classes. The experimental results show the positive effect of the proposed system in improving the road crossing behavior of the children.

PALOMAGE, a French real-world cohort of elderly women beyond age 70 with advanced breast cancer receiving palbociclib: Baseline characteristics and safety evaluation.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1012-1012
Author(s):  
Philippe Caillet ◽  
Marina Pulido ◽  
Etienne Brain ◽  
Claire Falandry ◽  
Isabelle Desmoulins ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Advanced Breast Cancer ◽  
Real World ◽  
Older Patients ◽  
Systemic Treatment ◽  
Safety Data ◽  
Real Life ◽  
Advanced Breast ◽  
Baseline Characteristics ◽  
Grade 3

1012 Background: Advanced breast cancer (ABC) is common in older patients, resulting from the high incidence of breast cancer beyond age 70. This population is often limited in clinical trials. Endocrine therapy (ET) combined with a CDK4/6 inhibitor is the standard of care in ABC overexpressing hormonal receptors (HR+). Data specific to older patients are scarce in the literature, deserving further research. Methods: PALOMAGE is an ongoing French prospective study evaluating palbociclib (PAL) + ET in real life setting in women aged ≥70 with HR+ HER2- ABC, split in 2 cohorts: ET sensitive patients with no prior systemic treatment for ABC (cohort A), and ET resistant patients and/or with prior systemic treatment for ABC (cohort B). Data collected include clinical characteristics, quality of life (EORTC QLQ-C30 and ELD14) and geriatric description [G8 and Geriatric-COre DatasEt (G-CODE)]. This analysis reports on baseline characteristics and safety data for the whole population. Results: From 10/2018 to 10/2020, 400 and 407 patients were included in cohort A and B, respectively. The median age was 79 years (69-98), 15.1% with an age > 85. ECOG performance status (PS) was ≥2 in 17.9% patients, 68.3% had a G8 score ≤14 suggesting frailty, 32.1% had bone only metastasis, and 44% had visceral disease. 35.8% of patients in cohort B had no prior treatment for ABC. Safety data were available for 787 patients. The median follow-up was 6.7 months (IC95% = 6.1-7.6). At start of treatment, full dose of PAL (125 mg) was used in 76% of the patients: 62.6%, 68.7% and 71.6% of patients aged ≥ 80, those with ECOG PS ≥2 and those with a G8 score ≤14, respectively. In the safety population, 70% had ≥1 adverse event (AE), including 43.1% grade 3/4 AE, and 22.9% ≥ 1 serious AE. Most frequent AE reported were neutropenia (43.2%), anemia (17.5%), asthenia (16.3%) and thrombocytopenia (13.6%). Grade 3/4 neutropenia was observed in 32.3% of patients, with febrile neutropenia in 1.1%. Grade 3/4 AE PAL-related were reported in 40.1%, 31.4% of patients aged < 80, ≥80, respectively. Regarding PAL, 23.4% of patients had a dose reduction and 41.8% had a temporary or permanent discontinuation due to AE. Safety data were similar in both cohorts. Geriatric data and impact on safety will be presented. Conclusions: PALOMAGE is a unique large real-world cohort focusing on older patients treated with PAL in France. These preliminary data do not suggest any new safety signal, matching data derived from PALOMA trials. The occurrence of less grade3/4 AE related to PAL in patients aged 80 and beyond might reflect the 30% decrease of PAL dose upfront. Effectiveness analyses are eagerly awaited. Clinical trial information: EUPAS23012 .

scholarly journals Experimentally-induced and real-world acute anxiety have no effect on goal-directed behaviour

2019 ◽  
Author(s):  
CM Gillan ◽  
MM Vaghi ◽  
FH Hezemans ◽  
Grothe S van Ghesel ◽  
J Dafflon ◽  
...  
Keyword(s):  
Individual Differences ◽  
Anxiety Disorders ◽  
Healthy Volunteers ◽  
Real World ◽  
Real Life ◽  
Stressful Events ◽  
Panic Attacks ◽  
Healthy Individuals ◽  
Correlational Design ◽  
Acute Anxiety

AbstractCompulsivity is associated with failures in goal-directed control, an important cognitive faculty that protects against developing habits. But might this effect be explained by co-occurring anxiety? Previous studies have found goal-directed deficits in other anxiety disorders, and to some extent when healthy individuals are stressed, suggesting this is plausible. We carried out a causal test of this hypothesis in two experiments (between-subject N=88; within-subject N=50) that used the inhalation of hypercapnic gas (7.5% CO2) to induce an acute state of anxiety in healthy volunteers. In both experiments, we successfully induced anxiety, assessed physiologically and psychologically, but this did not affect goal-directed performance. In a third experiment (N=1413), we used a correlational design to test if real-life anxiety-provoking events (panic attacks, stressful events) impair goal-directed control. While small effects were observed, none survived controlling for individual differences in compulsivity. These data suggest that anxiety has no meaningful impact on goal-directed control.

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