Next challenges for adaptive learning systems

Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other business data that arrives in real time and needs to be mined in real time. Under such circumstances, constant manual adjustment of models is in-efficient and with increasing amounts of data is becoming infeasible. Nevertheless, adaptive learning models are still rarely employed in business applications in practice. In the light of rapidly growing structurally rich 'big data', new generation of parallel computing solutions and cloud computing services as well as recent advances in portable computing devices, this article aims to identify the current key research directions to be taken to bring the adaptive learning closer to application needs. We identify six forthcoming challenges in designing and building adaptive learning (pre-diction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrat-ing expert knowledge, taking into account various application needs, and moving from adaptive algorithms towards adaptive tools. Those challenges are critical for the evolving stream settings, as the process of model building needs to be fully automated and continuous.</jats:p

Spectroscopy of a fractional Josephson vortex molecule

In long Josephson junctions with multiple discontinuities of the Josephson phase, fractional vortex molecules are spontaneously formed. At each discontinuity point a fractional Josephson vortex carrying a magnetic flux $|\Phi|<\Phi_0$, $\Phi_0\approx 2.07\times 10^{-15}$ Wb being the magnetic flux quantum, is pinned. Each vortex has an oscillatory eigenmode with a frequency that depends on $\Phi/\Phi_0$ and lies inside the plasma gap. We experimentally investigate the dependence of the eigenfrequencies of a two-vortex molecule on the distance between the vortices, on their topological charge $\wp=2\pi\Phi/\Phi_0$ and on the bias current $\gamma$ applied to the Josephson junction. We find that with decreasing distance between vortices, a splitting of the eigenfrequencies occurs, that corresponds to the emergence of collective oscillatory modes of both vortices. We use a resonant microwave spectroscopy technique and find good agreement between experimental results and theoretical predictions.Comment: submitted to Phys. Rev.

Hybrid Deep Neural Network for Facial Expressions Recognition

Facial expressions are critical indicators of human emotions where recognizing facial expressions has captured the attention of many academics, and recognition of expressions in natural situations remains a challenge due to differences in head position, occlusion, and illumination. Several studies have focused on recognizing emotions from frontal images only, while in this paper wild images from the FER2013 dataset have been used to make a more generalizing model with the existence of its challenges, it is among the most difficult datasets that only got 65.5 % accuracy human-level. This paper proposed a model for recognizing facial expressions using pre-trained deep convolutional neural networks and the technique of transfer learning. this hybrid model used a combination of two pre-trained deep convolutional neural networks, training the model in multiple cases for more efficiency to categorize the facial expressions into seven classes. The results show that the best accuracy of the suggested models is 74.39%  for the hybrid model, and 73.33% for Fine-tuned the single EfficientNetB0 model, while the highest accuracy for previous methods was 73.28%. Thus, the hybrid and single models outperform other state of art classification methods without using any additional, the hybrid and single models ranked in the first and second position among these methods. Also, The hybrid model has even outperformed the second-highest in accuracy method which used extra data. The incorrectly labeled images in the dataset unfairly reduce accuracy but our best model recognized their actual classes correctly

An efficient broadcasting routing protocol WAODV in mobile ad hoc networks

Information broadcasting in wireless network is a necessary building block for cooperative operations. However, the broadcasting causes increases the routing overhead. This paper brings together an array of tools of our adaptive protocol for information broadcasting in MANETs. The proposed protocol in this paper named WAODV (WAIT-AODV). This new adaptive routing discovery protocol for MANETs, lets in nodes to pick out a fantastic motion: both to retransmit receiving request route request (RREQ) messages, to discard, or to wait earlier than making any decision, which dynamically confgures the routing discovery feature to decide a gorgeous motion through the usage of neighbors’ knowledge. Simulations have been conducted to show the effectiveness of the using of techniques adaptive protocol for information broadcasting RREQ packet when integrated into ad hoc on-demand distance vector (AODV) routing protocols for MANET (which is based on simple flooding)

Spectroscopy of the fractional vortex eigenfrequency in a long Josephson 0-kappa junction

Fractional Josephson vortices carry a magnetic flux Phi, which is a fraction of the magnetic flux quantum Phi_0 ~ 2.07x10^{-15} Wb. Their properties are very different from the properties of the usual integer fluxons. In particular, fractional vortices are pinned and have an oscillation eigenfrequency which is expected to be within the Josephson plasma gap. Using microwave spectroscopy, we investigate the dependence of the eigenfrequency of a fractional Josephson vortex on its magnetic flux $\Phi$ and on the bias current. The experimental results are in good agreement with the theoretical predictions.Comment: submitted to PR

Nanocoating with plant-derived pectins activates osteoblast response in vitro

Abstract: A new strategy to improve osseointegration of implants is to stimulate adhesion of bone cells, bone matrix formation, and mineralization at the implant surface by modifying surface coating on the nanoscale level. Plant-derived pectins have been proposed as potential candidates for surface nanocoating of orthopedic and dental titanium implants due to 1) their osteogenic stimulation of osteoblasts to mineralize and 2) their ability to control pectin structural changes. The aim of this study was to evaluate in vitro the impact of the nanoscale plant-derived pectin Rhamnogalacturonan-I (RG-I) from potato on the osteogenic response of murine osteoblasts. RG-I from potato pulps was isolated, structurally modified, or left unmodified. Tissue culture plates were either coated with modified RG-I or unmodified RG-I or – as a control – left uncoated. The effect of nanocoating on mice osteoblast- like cells MC3T3-E1 and primary murine osteoblast with regard to proliferation, osteogenic response in terms of mineralization, and gene expression of Runt-related transcription factor 2 (Runx2), alkaline phosphate (Alpl), osteocalcin (Bglap), α-1 type I collagen (Col1a1), and receptor activator of NF-κB ligand (Rankl) were analyzed after 3, 7, 14, and 21 days, respectively. Nanocoating with pectin RG-Is increased proliferation and mineralization of MC3T3-E1 and primary osteoblast as compared to osteoblasts cultured without nanocoating. Moreover, osteogenic transcriptional response of osteoblasts was induced by nanocoating in terms of gene induction of Runx2, Alpl, Bglap, and Col1a1 in a time-dependent manner – of note – to the highest extent under the PA-coating condition. In contrast, Rankl expression was initially reduced by nanocoating in MC3T3-E1 or remained unaltered in primary osteoblast as compared to the uncoated controls. Our results showed that nanocoating of implants with modified RG-I beneficially 1) supports osteogenesis, 2) has the capacity to improve osseointegration of implants, and is therefore 3) a potential candidate for nanocoating of bone implants

Thermal escape of fractional vortices in long Josephson junctions

We consider a fractional Josephson vortex in a long 0-kappa Josephson junction. A uniformly applied bias current exerts a Lorentz force on the vortex. If the bias current exceeds the critical current, an integer fluxon is torn off the kappa-vortex and the junction switches to the voltage state. In the presence of thermal fluctuations the escape process takes place with finite probability already at subcritical values of the bias current. We experimentally investigate the thermally induced escape of a fractional vortex by high resolution measurements of the critical current as a function of the topological charge kappa of the vortex and compare the results to numerical simulations for finite junction lengths and to theoretical predictions for infinite junction lengths. To study the effect caused by the junction geometry we compare the vortex escape in annular and linear junctions.Comment: submitted to PR

Novel Pressure Induced Structural Phase Transition in AgSbTe2_{2}

We report a novel high pressure structural sequence for the functionally graded thermoelectric, narrow band gap semiconductor AgSbTe$_{2}$, using angle dispersive x-ray diffraction in a diamond anvil cell with synchrotron radiation at room temperature. The compound undergoes a B1 to B2 transition; the transition proceeds through an intermediate amorphous phase found between 17-26 GPa that is quenchable down to ambient conditions. The pressure induced structural transition observed in this compound is the first of its type reported in this ternary cubic family, and it is new for the B1-B2 transition pathway reported to date. Density Functional Theory (DFT) calculations performed for the B1 and B2 phases are in good agreement with the experimental results.Comment: 4 pages, 3 figure