Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Layer Scheduling and Beamforming in Smart-Grid Powered Cellular Networks With Heterogeneous Energy Coordination

User scheduling, beamforming and energy coordination are investigated in smart-grid powered cellular networks (SGPCNs), where the base stations are powered by a smart grid and natural renewable energy sources. Heterogeneous energy coordination is considered in SGPCNs, namely energy merchandizing with the smart grid and energy exchanging among the base stations. A long-term grid-energy expenditure minimization problem with proportional-rate constraints is formulated for SGPCNs. Since user scheduling is coupled with the beamforming vectors, the formulated problem is challenging to handle via standard convex optimization methods. In practice, the beamforming vectors need to be updated over each slot according to the channel variations. User scheduling needs to be updated over several slots (frame) since the frequent scheduling of user equipment can cause reliability issues. Therefore, the Lyapunov optimization method is used to decouple the problem. A practical two-scale algorithm is proposed to schedule users at each frame, and obtain the beamforming vectors and amount of exchanged natural renewable energy at each slot. We prove that the proposed two-scale algorithm can asymptotically achieve the optimal solutions via tuning a control parameter. Numerical results verify the performance of the proposed two-scale algorithm.
Towards Multi-perspective Conformance Checking with Fuzzy Sets

A crucial issue for today’s organizations is to ensure that the executions of their processes comply with a set of constraints like, e.g., internal managerial choices or external legal requirements.Conformance checking techniques are widely adopted to monitor the execution of the organization processes and pinpoint possible discrepancies with respect to the prescribed behaviors. However, state of the art approaches adopt a crisp evaluation of deviations, with the result that small violations are considered at the same level of significant ones. This affects the quality of the provided diagnostics, especially when there exists some tolerance with respect to reasonably small violations, and hampers the flexibility of the process. In this work, we propose a novel approach which allows to represent actors’tolerance with respect to violations and to account for severity of deviations when assessing executions compliance. Besides improving the quality of the provided diagnostics, allowing some tolerance in deviation assessment also enhances the flexibility of conformance checking techniques and, indirectly, paves the way for improving the resilience of the overall process management system.
Constructing a subgradient from directional derivatives for functions of two variables

For any scalar-valued bivariate function that is locally Lipschitz continuous and directionally differentiable, it is shown that a subgradient may always be constructed from the function's directional derivatives in the four compass directions, arranged in a so-called "compass difference". When the original function is nonconvex, the obtained subgradient is an element of Clarke's generalized gradient, but the result appears to be novel even for convex functions. The function is not required to be represented in any particular form, and no further assumptions are required, though the result is strengthened when the function is additionally L-smooth in the sense of Nesterov. For certain optimal-value functions and certain parametric solutions of differential equation systems, these new results appear to provide the only known way to compute a subgradient. These results also imply that centered finite differences will converge to a subgradient for bivariate nonsmooth functions. As a dual result, we find that any compact convex set in two dimensions contains the midpoint of its interval hull. Examples are included for illustration, and it is demonstrated that these results do not extend directly to functions of more than two variables or sets in higher dimensions.
Two-component axionic dark matter halos

We consider a two-component dark matter halo (DMH) of a galaxy containing ultra-light axions (ULA) of different mass. The DMH is described as a Bose-Einstein condensate (BEC) in its ground state. In the mean-field (MF) limit we have derived the integro-differential equations for the spherically symmetrical wave functions of the two DMH components. We studied numerically the radial distribution of the mass density of ULA and constructed the parameters which could be used to distinguish between the two- and one-component DMH. We also discuss an interesting connection between the BEC MF ground state of a one-component DMH and Hawking temperature and entropy of a corresponding Black Hole, and Unruh temperature.
Single-Step Modified Electrodes for Vitamin C Monitoring in Sweat

We demonstrate a flexible sensor for ascorbic acid detection in sweat based on single-step modified gold microelectrodes. The modification consists on the electrodeposition of alginate membrane with trapped CuO nanoparticles on top of the electrodes. The electrodes are fabricated at a thin polyimide support and the soft nature of the membrane can withstand mechanical stress far beyond the requirements for skin monitoring. We further show the efficient detection of ascorbic acid at the micromolar levels in both, a neutral buffer and acidic artificial sweat, at ultra-low applied potential (-5 mV). The effect of possible interfering species present in sweat is minimized, with no observable cross-reaction, thus maintaining a high degree of selectivity despite absence of enzymes in the fabrication scheme. This sensor is envisioned as a promising component of a wearable device for e.g. non-invasive monitoring of micronutrient loss through sweat.
On Learning Vehicle Detection in Satellite Video

Vehicle detection in aerial and satellite images is still challenging due to their tiny appearance in pixels compared to the overall size of remote sensing imagery. Classical methods of object detection very often fail in this scenario due to violation of implicit assumptions made such as rich texture, small to moderate ratios between image size and object size. Satellite video is a very new modality which introduces temporal consistency as inductive bias. Approaches for vehicle detection in satellite video use either background subtraction, frame differencing or subspace methods showing moderate performance (0.26 - 0.82 $F_1$ score). This work proposes to apply recent work on deep learning for wide-area motion imagery (WAMI) on satellite video. We show in a first approach comparable results (0.84 $F_1$) on Planet's SkySat-1 LasVegas video with room for further improvement.
Developing a gender classification approach in human face images using modified local binary patterns and tani-moto based nearest neighbor algorithm

Human identification is a much attention problem in computer vision. Gender classification plays an important role in human identification as preprocess step. So far, various methods have been proposed to solve this problem. Absolutely, classification accuracy is the main challenge for researchers in gender classification. But, some challenges such as rotation, gray scale variations, pose, illumination changes may be occurred in smart phone image capturing. In this respect, a multi step approach is proposed in this paper to classify genders in human face images based on improved local binary patters (MLBP). LBP is a texture descriptor, which extract local contrast and local spatial structure information. Some issues such as noise sensitivity, rotation sensitivity and low discriminative features can be considered as disadvantages of the basic LBP. MLBP handle disadvantages using a new theory to categorize extracted binary patterns of basic LBP. The proposed approach includes two stages. First of all, a feature vector is extracted for human face images based on MLBP. Next, non linear classifiers can be used to classify gender. In this paper nearest neighborhood classifier is evaluated based on Tani-Moto metric as distance measure. In the result part, two databases, self-collected and ICPR are used as human face database. Results are compared by some state-ofthe-art algorithms in this literature that shows the high quality of the proposed approach in terms of accuracy rate. Some of other main advantages of the proposed approach are rotation invariant, low noise sensitivity, size invariant and low computational complexity. The proposed approach decreases the computational complexity of smartphone applications because of reducing the number of database comparisons. It can also improve performance of the synchronous applications in the smarphones because of memory and CPU usage reduction.
Submodular Rank Aggregation on Score-Based Permutations for Distributed Automatic Speech Recognition

Distributed automatic speech recognition (ASR) requires to aggregate outputs of distributed deep neural network (DNN)-based models. This work studies the use of submodular functions to design a rank aggregation on score-based permutations, which can be used for distributed ASR systems in both supervised and unsupervised modes. Specifically, we compose an aggregation rank function based on the Lovasz Bregman divergence for setting up linear structured convex and nested structured concave functions. The algorithm is based on stochastic gradient descent (SGD) and can obtain well-trained aggregation models. Our experiments on the distributed ASR system show that the submodular rank aggregation can obtain higher speech recognition accuracy than traditional aggregation methods like Adaboost. Code is available online 1.
Deep Learning in Multi-organ Segmentation

This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications. These methods were classified into six categories according to their network design. For each category, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review of each category, we briefly discussed its achievements, shortcomings and future potentials. We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge datasets and 2015 MICCAI Head Neck Auto-Segmentation Challenge datasets.
Outflows and extended [C ii] haloes in high-redshift galaxies

Recent stacked ALMA observations have revealed that normal, star-forming galaxies at z ≈ 6 are surrounded by extended (≈10 kpc) [C ii]-emitting haloes, which are not predicted by the most advanced, zoom-in simulations. We present a model in which these haloes are the result of supernova-driven cooling outflows. Our model contains two free parameters, the outflow mass loading factor, η, and the parent galaxy dark matter halo circular velocity, vc. The outflow model successfully matches the observed [C ii] surface brightness profile if η = 3.20 ± 0.10 and $v_{\rm c} = 170 \pm 10 \, \rm km\, s^{-1}$, corresponding to a dynamical mass of ${\approx }10^{11}\, {\rm M}_{\odot }$. The predicted outflow rate and velocity range are $128 \pm 5\, {\rm M}_{\odot }\, {\rm yr}^{-1}$ and 300–500 $\, \rm km\, s^{-1}$, respectively. We conclude that (a) extended haloes can be produced by cooling outflows; (b) the large η value is marginally consistent with starburst-driven outflows, but it might indicate additional energy input from active galactic nuclei; and (c) the presence of [C ii] haloes requires an ionizing photon escape fraction from galaxies fesc ≪ 1. The model can be readily applied also to individual high-z galaxies, as those observed, e.g. by the ALMA ALPINE survey now becoming available.
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs

Abstract Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are directly connected. In contrast, local node encoding considers the relations between neighbor nodes capturing the graph structure, but it can fail to capture long-range relations. In this work, we gather both encoding strategies, proposing novel neural models that encode an input graph combining both global and local node contexts, in order to learn better contextualized node embeddings. In our experiments, we demonstrate that our approaches lead to significant improvements on two graph-to-text datasets achieving BLEU scores of 18.01 on the AGENDA dataset, and 63.69 on the WebNLG dataset for seen categories, outperforming state-of-the-art models by 3.7 and 3.1 points, respectively.1
Radial velocity constraints on the long-period transiting planet Kepler-1625 b with CARMENES

Context. The star Kepler-1625 recently attracted considerable attention when an analysis of the stellar photometric time series from the Kepler mission was interpreted as showing evidence of a large exomoon around the transiting Jupiter-sized planet candidate Kepler-1625 b. However, the mass of Kepler-1625 b has not been determined independently and its planetary nature has not been formally validated. Moreover, Kepler’s long-period Jupiter-sized planet candidates, like Kepler-1625 b with an orbital period of about 287 d, are known to have a high false-alarm probability. An independent confirmation of Kepler-1625 b is therefore particularly important. Aims. We aim to detect the radial velocity (RV) signal imposed by Kepler-1625 b (and its putative moon) on the host star, or, as the case may be, determine an upper limit on the mass of the transiting object (or the combined mass of the two objects). Methods. We took a total of 22 spectra of Kepler-1625 using CARMENES, 20 of which were useful. Observations were spread over a total of seven nights between October 2017 and October 2018, covering 125% of one full orbit of Kepler-1625 b. We used the automatic Spectral Radial Velocity Analyser pipeline to deduce the stellar RVs and uncertainties. We then fitted the RV curve model of a single planet on a Keplerian orbit to the observed RVs using a χ2 minimisation procedure. Results. We derive upper limits on the mass of Kepler-1625 b under the assumption of a single planet on a circular orbit. In this scenario, the 1σ, 2σ, and 3σ confidence upper limits for the mass of Kepler-1625 b are 2.90 MJ, 7.15 MJ, and 11.60 MJ, respectively (MJ being Jupiter’s mass). An RV fit that includes the orbital eccentricity and orientation of periastron as free parameters also suggests a planetary mass but is statistically less robust. Conclusions. We present strong evidence for the planetary nature of Kepler-1625 b, making it the (confirmed) planet with the tenth longest period known today. Our data do not allow us to make any form conclusions regarding a second, possibly shorter period planet that could be responsible for the observed transit timing variation of Kepler-1625 b.
Blockchain Enabled Smart Contract Based Applications: Deficiencies with the Software Development Life Cycle Models

With the recent popularity of Blockchain and other Distributed Ledger Technologies (DLT), blockchain enabled smart contract applications has attracted increased research focus. However, the immutability of the blocks, where the smart contracts are stored, causes conflicts with the traditional Software Development Life Cycle (SDLC) models usually followed by software engineers. This clearly shows the unsuitability of the application of SDLC in designing blockchain enabled smart contract based applications. This research article addresses this current problem by first exploring the six traditional SDLC models, clearly identifying the conflicts in a table with the application of smart contracts and advocates that there is an urgent need to develop new standard model(s) to address the arising issues. The concept of both block immutability and contract is introduced. This is further set in a historical context from legacy smart contracts and blockchain enabled smart contracts extending to the difference between "shallow smart contracts" and "deep smart contracts". To conclude, the traditional SDLC models are unsuitable for blockchain enabled smart contract-based applications.
Minimal non uniquely ergodic flipped IETs

In this paper we prove the existence of minimal non uniquely ergodic flipped IETs. In particular, we build explicitly minimal non uniquely ergodic $(10,k)$-IETs for any $1\leq k \leq 10$. This answers an open question posed in [C. ~Gutierrez, S. ~Lloyd, V. ~Medvedev, B. ~Pires, and E. ~Zhuzhoma.Transitive circle exchange transformations with flips. Discrete Contin. Dyn. Syst. ,26 (1):251--263, 2010]. As a consequence, we also derive the existence of transitive non uniquely ergodic $(n,k)$-IETs, for any $n\geq 10$ and $1\leq k\leq n$ if $n$ is even, and $1\leq k\leq n-1$ if $n$ is odd.
Habitable Zones around Almost Extremely Spinning Black Holes (Black Sun Revisited)

We analyzed the thermodynamics of hypothetical exoplanets at very low Keplerian circular orbits in close vicinity of rapidly spinning supermassive black holes. Such black hole exoplanets are heated by strongly blueshifted and focused flux of the incoming cosmic microwave background (CMB) and cooled by the cold part of the local sky containing the black hole shadow. This gives rise to a temperature difference, which can drive processes far from thermodynamic equilibrium in a hypothetical life form inhabiting black hole exoplanets, similar to the case of a planet heated by the radiation of the parent star and cooled by the night sky. We found that for a narrow range of radii of very low Keplerian circular orbits and for very high spin of a supermassive black hole, the temperature regime of the black hole exoplanets corresponds to the habitable zone around standard stars. The thermodynamics of black hole exoplanets therefore, in principle, does not exclude the existence of life based on known biology. The peak of the multiblackbody spectral profile of the CMB heating the exoplanet is located in the ultraviolet band, but a significant fraction of the flux comes also in the visible and infrared bands. The minimum mass of a black hole ensuring the resistance to tidal disruption of an Earth-like exoplanet orbiting in the habitable zone is estimated to 1.63 · 108 m⊙.
Reducing complexity and unidentifiability when modelling human atrial cells

Mathematical models of a cellular action potential (AP) in cardiac modelling have become increasingly complex, particularly in gating kinetics, which control the opening and closing of individual ion channel currents. As cardiac models advance towards use in personalized medicine to inform clinical decision-making, it is critical to understand the uncertainty hidden in parameter estimates from their calibration to experimental data. This study applies approximate Bayesian computation to re-calibrate the gating kinetics of four ion channels in two existing human atrial cell models to their original datasets, providing a measure of uncertainty and indication of potential issues with selecting a single unique value given the available experimental data. Two approaches are investigated to reduce the uncertainty present: re-calibrating the models to a more complete dataset and using a less complex formulation with fewer parameters to constrain. The re-calibrated models are inserted back into the full cell model to study the overall effect on the AP. The use of more complete datasets does not eliminate uncertainty present in parameter estimates. The less complex model, particularly for the fast sodium current, gave a better fit to experimental data alongside lower parameter uncertainty and improved computational speed. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.
Classification of Strongly Disordered Topological Wires Using Machine Learning

In this article we apply the random forest machine learning model to classify 1D topological phases when strong disorder is present. We show that using the entanglement spectrum as training features the model gives high classification accuracy. The trained model can be extended to other regions in phase space, and even to other symmetry classes on which it was not trained and still provides accurate results. After performing a detailed analysis of the trained model we find that its dominant classification criteria captures degeneracy in the entanglement spectrum.
Quantitative sub-resolution blood velocity estimation using ultrasound localization microscopy ex-vivo and in-vivo

Super-resolution ultrasound imaging relies on the sub-wavelength localization of microbubble contrast agents. By tracking individual microbubbles, the velocity and flow within microvessels can be estimated. It has been shown that the average flow velocity, within a microvessel ranging from tens to hundreds of microns in diameter, can be measured. However, a 2D super-resolution image can only localize bubbles with sub-wavelength resolution in the imaging plane whereas the resolution in the elevation plane is limited by conventional beamwidth physics. Since ultrasound imaging integrates echoes over the elevation dimension, velocity estimates at a single location in the imaging plane include information throughout the imaging slice thickness. This slice thickness is typically a few orders or magnitude larger than the super-resolution limit. It is shown here that in order to estimate the velocity, a spatial integration over the elevation direction must be considered. This operation yields a multiplicative correction factor that compensates for the elevation integration. A correlation-based velocity estimation technique is then presented. Calibrated microtube phantom experiments are used to validate the proposed velocity estimation method and the proposed elevation integration correction factor. It is shown that velocity measurements are in excellent agreement with theoretical predictions within the considered range of flow rates (10 to 90 μl/min) in a microtube with a diameter of 200 μm. Then, the proposed technique is applied to two in-vivo mouse tail experiments imaged with a low frequency human clinical transducer (ATL L7-4) with human clinical concentrations of microbubbles. In the first experiment, a vein was visible with a diameter of 140 μm and a peak flow velocity of 0.8 mm s−1. In the second experiment, a vein was observed in the super-resolved image with a diameter of 120 μm and with maximum local velocity of ≈4.4 mm s−1. It is shown that the parabolic flow profiles within these micro-vessels are resolvable.
Metamaterials and Cesàro convergence

In this paper, we show that the linear dielectrics and magnetic materials in matter obey a special kind of mathematical property known as Ces\`{a}ro convergence. Then, we also show that the analytical continuation of the linear permittivity \& permeability to a complex plane in terms of Riemann zeta function. The metamaterials are fabricated materials with a negative refractive index. These materials, in turn, depend on permittivity \& permeability of the linear dielectrics and magnetic materials. Therefore, the Ces\`{a}ro convergence property of the linear dielectrics and magnetic materials may be used to fabricate the metamaterials.
Secure quantum key distribution with realistic devices

Some years ago quantum hacking became popular: devices implementing the unbreakable quantum cryptography were shown to have imperfections which could be exploited by attackers. Security has been thoroughly enhanced, as a consequence of both theoretical and experimental advances. This review gives both sides of the story, with the current best theory of quantum security, and an extensive survey of what makes quantum cryptosystem safe in practice.