Understanding the internet topology evolution dynamics

The internet structure is extremely complex. The Positive-Feedback Preference (PFP) model is a recently introduced internet topology generator. The model uses two generic algorithms to replicate the evolution dynamics observed on the internet historic data. The phenomenological model was originally designed to match only two topology properties of the internet, i.e. the rich-club connectivity and the exact form of degree distribution. Whereas numerical evaluation has shown that the PFP model accurately reproduces a large set of other nontrivial characteristics as well. This paper aims to investigate why and how this generative model captures so many diverse properties of the internet. Based on comprehensive simulation results, the paper presents a detailed analysis on the exact origin of each of the topology properties produced by the model. This work reveals how network evolution mechanisms control the obtained topology properties and it also provides insights on correlations between various structural characteristics of complex networks.Comment: 15 figure

Whistle detection and classification for whales based on convolutional neural networks

Passive acoustic observation of whales is an increasingly important tool for whale research. Accurately detecting whale sounds and correctly classifying them into corresponding whale species are essential tasks, especially in the case when two species of whales vocalize in the same observed area. Whistles are vital vocalizations of toothed whales, such as killer whales and long-finned pilot whales. In this paper, based on deep convolutional neural networks (CNNs), a novel method is proposed to detect and classify whistles of both killer whales and long-finned pilot whales. Compared with traditional methods, the proposed one can automatically learn the sound characteristics from the training data, without specifying the sound features for classification and detection, and thus shows better adaptability to complex sound signals. First, the denoised sound to be analyzed is sent to the trained detection model to estimate the number and positions of the target whistles. The detected whistles are then sent to the trained classification model, which determines the corresponding whale species. A GUI interface is developed to assist with the detection and classification process. Experimental results show that the proposed method can achieve 97% correct detection rate and 95% correct classification rate on the testing set. In the future, the presented method can be further applied to passive acoustic observation applications for some other whale or dolphin species

A Cloud-Edge-aided Incremental High-order Possibilistic c-Means Algorithm for Medical Data Clustering

Medical Internet of Things are generating a big volume of data to enable smart medicine that tries to offer computer-aided medical and healthcare services with artificial intelligence techniques like deep learning and clustering. However, it is a challenging issue for deep learning and clustering algorithms to analyze large medical data because of their high computational complexity, thus hindering the progress of smart medicine. In this paper, we present an incremental high-order possibilistic c-means algorithm on a cloud-edge computing system to achieve medical data co-clustering of multiple hospitals in different locations. Specifically, each hospital employs the deep computation model to learn a feature tensor of each medical data object on the local edge computing system and then uploads the feature tensors to the cloud computing platform. The high-order possibilistic c-means algorithm (HoPCM) is performed on the cloud system for medical data clustering on uploaded feature tensors. Once the new medical data feature tensors are arriving at the cloud computing platform, the incremental high-order possibilistic c-means algorithm (IHoPCM) is performed on the combination of the new feature tensors and the previous clustering centers to obtain clustering results for the feature tensors received to date. In this way, repeated clustering on the previous feature tensors is avoided to improve the clustering efficiency. In the experiments, we compare different algorithms on two medical datasets regarding clustering accuracy and clustering efficiency. Results show that the presented IHoPCM method achieves great improvements over the compared algorithms in clustering accuracy and efficiency

Spin measurement of 4U 1543-47 with Insight-HXMT and NICER from its 2021 outburst: A test of accretion disk models at high luminosities

4U 1543--47 is one of a handful of known black hole candidates located in the Milky Way Galaxy, and has undergone a very bright outburst in 2021, reaching a total of $\sim$9 Crab, as observed by the Monitor of All-sky Image (MAXI), and exceeding twice its Eddington luminosity. The unprecedented bright outburst of 4U 1543--47 provides a unique opportunity to test the behavior of accretion disk models at high luminosities and accretion rates. In addition, we explore the possibility of constraining the spin of the source at high accretion rates, given that previous spin measurements of 4U 1543--47 have been largely inconsistent with each other. We measure the spectral evolution of the source throughout its outburst as observed by Insight-HXMT, and compare the behavior of both the thin disk model kerrbb2, as well as the slim disk model slimbh up to the Eddington limit for two different values of disk $\alpha$-viscosity. In addition, given the behavior of these two models, we identify two `golden' epochs for which it is most suitable to measure the spin with continuum fitting.Comment: 10 pages, 6 figure

Detection of a strong ~2.5 Hz modulation in the Newly Discovered Millisecond Pulsar MAXI J1816-195

MAXI J181-195 is a newly discovered accreting millisecond X-ray pulsar that went outburst in June 2022. Through timing analysis with NICER and NuSTAR observations, we find a transient modulation at ~2.5 Hz during the decay period of MAXI J1816-195. The modulation is strongly correlated with a spectral hardening, and its fractional rms amplitude increases with energy. These results suggest that the modulation is likely to be produced in an unstable corona. In addition, the presence of the modulation during thermonuclear bursts indicates that it may originate from a disk-corona where the optical depth is likely the main factor affecting the modulation, rather than temperature. Moreover, we find significant reflection features in the spectra observed simultaneously by NICER and NuSTAR, including a relativistically broadened Fe-K line around 6-7 keV, and a Compton hump in the 10-30 keV energy band. The radius of the inner disc is constrained to be Rin = (1.04-1.23) RISCO based on reflection modeling of the broadband spectra. Assuming that the inner disc is truncated at the magnetosphere radius, we estimate that the magnetic field strength is < 4.67 * 10e8 G.Comment: 12 pages, 13 figure

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

Synthesis and Properties of c-axis Oriented Epitaxial MgB2 Thin Films

We report the growth and properties of epitaxial MgB2 thin films on (0001) Al2O3 substrates. The MgB2 thin films were prepared by depositing boron films via RF magnetron sputtering, followed by a post-deposition anneal at 850C in magnesium vapor. X-ray diffraction and cross-sectional TEM reveal that the epitaxial MgB2 films are oriented with their c-axis normal to the (0001) Al2O3 substrate and a 30 degree rotation in the ab-plane with respect to the substrate. The critical temperature was found to be 35 K and the anisotropy ratio, Hc2(parallel to the film) / Hc2(pendicular to the film), about 3 at 25K. The critical current densities at 4.2 K and 20 K (at 1 T perpendicular magnetic field) are 5x10E6 A/cm2 and 1x10E6 A/cm2, respectively. The controlled growth of epitaxial MgB2 thin films opens a new avenue in both understanding superconductivity in MgB2 and technological applications.Comment: 10 pages, 6 figure

Very high upper critical fields in MgB2 produced by selective tuning of impurity scattering

We report a significant enhancement of the upper critical field $H_{c2}$ of different $MgB_2$ samples alloyed with nonmagnetic impurities. By studying films and bulk polycrystals with different resistivities $\rho$, we show a clear trend of $H_{c2}$ increase as $\rho$ increases. One particular high resistivity film had zero-temperature $H_{c2}(0)$ well above the $H_{c2}$ values of competing non-cuprate superconductors such as $Nb_3Sn$ and Nb-Ti. Our high-field transport measurements give record values $H_{c2}^\perp (0) \approx 34T$ and $H_{c2}\|(0) \approx 49 T$ for high resistivity films and $H_{c2}(0)\approx 29 T$ for untextured bulk polycrystals. The highest $H_{c2}$ film also exhibits a significant upward curvature of $H_{c2}(T)$, and temperature dependence of the anisotropy parameter $\gamma(T) = H_{c2}\|/ H_{c2}^\perp$ opposite to that of single crystals: $\gamma(T)$ decreases as the temperature decreases, from $\gamma(T_c) \approx 2$ to $\gamma(0) \approx 1.5$. This remarkable $H_{c2}$ enhancement and its anomalous temperature dependence are a consequence of the two-gap superconductivity in $MgB_2$, which offers special opportunities for further $H_{c2}$ increase by tuning of the impurity scattering by selective alloying on Mg and B sites. Our experimental results can be explained by a theory of two-gap superconductivity in the dirty limit. The very high values of $H_{c2}(T)$ observed suggest that $MgB_2$ can be made into a versatile, competitive high-field superconductor.Comment: An updated version of the paper (12/12/2002)that was placed on cond-mat on May 7 200