The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena

The Internet is the most complex system ever created in human history. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, large-scale effects of worm traffic, and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex System

Physics-Informed Neural Networks for the Condition Monitoring of Rotating Shafts

Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a dominant approach due to its capacity to capture intricate data patterns and relationships. However, a prevalent challenge lies in the black-box nature of many deep learning algorithms, which often operate without adhering to the underlying physical characteristics intrinsic to the studied phenomena. To address this limitation and enhance the fusion of data-driven methodologies with the fundamental physics of the system under study, this paper leverages physics-informed neural networks (PINNs). Specifically, a simple but realistic numerical case study of an extended Jeffcott rotor model, encompassing damping effects and anisotropic supports for a more comprehensive modelling, is considered. PINNs are used for the estimation of five parameters that characterize the health state of the system. These parameters encompass the radial and angular position of the static unbalance due to the disk installed on the shaft, the stiffness along the principal axes of elasticity, and the non-rotating damping coefficient. The estimation is conducted solely by exploiting the displacement signals from the centre of the disk and, to showcase the efficacy and precision provided by this novel methodology, various scenarios involving different constant rotational speeds are examined. Additionally, the impact of noisy input data is also taken into account within the analysis and the performance is compared to that of traditional optimization algorithms used for parameters estimation

Unsupervised Damage Localization In Composite Plates Using Lamb Waves And Conditional Generative Adversarial Networks

Composite plates are increasingly used in several engineering fields. A common way for monitoring the health state of these structures is by analysing ultrasonic guided waves propagating in the plate. Among guided waves, Lamb waves (LWs) have shown promising diagnostic capabilities, and have been recently used for damage diagnosis in deep learning-based frameworks. However, so far, the proposed frameworks have mainly leveraged supervised algorithms, which require acquiring and labelling a large amount of data when the structure is in healthy and damaged conditions. Besides requiring much time and effort, acquiring enough data in damaged structures may not be practical in real world. Hence, this paper proposes the use of conditional generative adversarial networks (CGANs) with convolutional layers for damage localization in composite plates. As unsupervised algorithms, CGANSs can be trained on LWs acquired when the structure is healthy, and do not require information about damaged states. The proposed method was validated through an experimental case study involving two different composite plates

Correlations between tumor-infiltrating and circulating lymphocyte subpopulations in advanced renal cancer patients treated with nivolumab

Background: In clinical trials with immunotherapy, histological features such as tumor-infiltrating lymphocytes (TILs) are investigated as potential predictive biomarkers, with the limit of an outdated parameter for a typically dynamic element. Methods: This explorative study compared, in metastatic renal cell carcinoma (mRCC) patients, basal pathological data about TILs on diagnostic histological specimens with circulating lymphocyte subpopulations measured before and during therapy with nivolumab. Results: Of 11 mRCC patients, 5 had low presence of TILs (L-TILs), 3 moderate amount (M-TILs) and 3 high number (H-TILs). Overall, 8 patients had low intratumoral pathological CD4+/CD8+ ratio (LIPR) ≤1 and 3 cases high intratumoral pathological ratio (HIPR) ≥2. Of 8 patients with LIPR, only 2 matched with low circulating CD4+/CD8+ ratio (LCR) ≤1; 5 had high circulating ratio (HCR) ≥2. All 3 cases with HIPR (≥2) conversely had LCR (≤1). Circulating CD4+/CD8+ ratio remained unchanged during therapy (mean-0.12 in 8 weeks). The respective percentage values of CD4+ and CD8+ circulating T cells also remained stable (variation 0%); the absolute value of CD4+ was more likely to increase (mean +46.3/mm3); the level of CD8+ tended to slightly decrease (mean-6.5/mm3). No correlation of lymphocyte subpopulations with treatment outcome was found. Of note, we did not evidence correspondence between histopathological and circulating findings in terms of T-lymphocyte subpopulations, also suggesting the inconsistency of circulating data in terms of relative variations. Conclusions: Considering the likely high dynamism of TILs, rebiopsy before therapy might be proposed to assess the utility of TILs characterization for predictive purpose. (www.actabiomedica.it)

Star Cluster Catalogs for the LEGUS Dwarf Galaxies

We present the star cluster catalogs for 17 dwarf and irregular galaxies in the $HST$ Treasury Program "Legacy ExtraGalactic UV Survey" (LEGUS). Cluster identification and photometry in this subsample are similar to that of the entire LEGUS sample, but special methods were developed to provide robust catalogs with accurate fluxes due to low cluster statistics. The colors and ages are largely consistent for two widely used aperture corrections, but a significant fraction of the clusters are more compact than the average training cluster. However, the ensemble luminosity, mass, and age distributions are consistent suggesting that the systematics between the two methods are less than the random errors. When compared with the clusters from previous dwarf galaxy samples, we find that the LEGUS catalogs are more complete and provide more accurate total fluxes. Combining all clusters into a composite dwarf galaxy, we find that the luminosity and mass functions can be described by a power law with the canonical index of $-2$ independent of age and global SFR binning. The age distribution declines as a power law, with an index of $\approx-0.80\pm0.15$, independent of cluster mass and global SFR binning. This decline of clusters is dominated by cluster disruption since the combined star formation histories and integrated-light SFRs are both approximately constant over the last few hundred Myr. Finally, we find little evidence for an upper-mass cutoff ($<2\sigma$) in the composite cluster mass function, and can rule out a truncation mass below $\approx10^{4.5}$M$_{\odot}$ but cannot rule out the existence of a truncation at higher masses

Star cluster catalogues for the LEGUS dwarf galaxies

We present a new Bayesian hierarchical model (BHM) named Steve for performing Type Ia supernova (SN Ia) cosmology fits. This advances previous works by including an improved treatment of Malmquist bias, accounting for additional sources of systematic uncertainty, and increasing numerical efficiency. Given light-curve fit parameters, redshifts, and host-galaxy masses, we fit Steve simultaneously for parameters describing cosmology, SN Ia populations, and systematic uncertainties. Selection effects are characterized using Monte Carlo simulations. We demonstrate its implementation by fitting realizations of SN Ia data sets where the SN Ia model closely follows that used in Steve. Next, we validate on more realistic SNANA simulations of SN Ia samples from the Dark Energy Survey and low-redshift surveys (DES Collaboration et al. 2018). These simulated data sets contain more than 60,000 SNe Ia, which we use to evaluate biases in the recovery of cosmological parameters, specifically the equation of state of dark energy, w. This is the most rigorous test of a BHM method applied to SN Ia cosmology fitting and reveals small w biases that depend on the simulated SN Ia properties, in particular the intrinsic SN Ia scatter model. This w bias is less than 0.03 on average, less than half the statistical uncertainty on w. These simulation test results are a concern for BHM cosmology fitting applications on large upcoming surveys; therefore, future development will focus on minimizing the sensitivity of Steve to the SN Ia intrinsic scatter model.AA acknowledges the support of the Swedish Research Council (Vetenskapsradet) and the Swedish National Space Board (SNSB). DAG acknowledges support by the German Aerospace Center (DLR) and the Federal Ministry for Economic Affairs and Energy (BMWi) through program 50OR1801 ‘MYSST: Mapping Young Stars in Space and Time’

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)