Miracle-dz/UniFAR-FTUs huggingface dataset:
MLC: A Machine Learning Based Checker For Soft Error Detection In Embedded Processors

With deep submicron scaling, the occurrence of soft errors has become a major reliability challenge for electronic systems. This work proposes a Machine Learning-based Checker (MLC) to protect hard-core processors against radiation-induced soft errors. MLC is an independent hardware unit that implements an ML algorithm to detect soft errors in a processor. The work presented here selects input features from key processor signals for creating a dataset for training. The dataset trains an ML model offline for learning the correct behavior of the processor and detecting soft errors at run-time. The inference of this trained ML is implemented in the MLC hardware that runs along with the processor. Several ML models have been considered for the inference phase, and XGBoost implementation has shown to be the best in terms of hardware overhead and accuracy. The proposed scheme is applied to a RISC-V-like processor, called SAYAC, as a case study.
Kinetic AGN feedback effects on cluster cool cores simulated using SPH

We implement novel numerical models of AGN feedback in the SPH code GADGET-3, where the energy from a supermassive black hole (BH) is coupled to the surrounding gas in the kinetic form. Gas particles lying inside a bi-conical volume around the BH are imparted a one-time velocity (10,000 km/s) increment. We perform hydrodynamical simulations of isolated cluster (total mass 10^14 /h M_sun), which is initially evolved to form a dense cool core, having central T<10^6 K. A BH resides at the cluster center, and ejects energy. The feedback-driven fast wind undergoes shock with the slower-moving gas, which causes the imparted kinetic energy to be thermalized. Bipolar bubble-like outflows form propagating radially outward to a distance of a few 100 kpc. The radial profiles of median gas properties are influenced by BH feedback in the inner regions (r<20-50 kpc). BH kinetic feedback, with a large value of the feedback efficiency, depletes the inner cool gas and reduces the hot gas content, such that the initial cool core of the cluster is heated up within a time 1.9 Gyr, whereby the core median temperature rises to above 10^7 K, and the central entropy flattens. Our implementation of BH thermal feedback (using the same efficiency as kinetic), within the star-formation model, cannot do this heating, where the cool core remains. The inclusion of cold gas accretion in the simulations produces naturally a duty cycle of the AGN with a periodicity of 100 Myr.
Smart City Management System Based on Multi-purpose Deep Neural Network

Modern research in the field of classification, detection and semantic segmentation focuses on the use of recurrent neural networks as the basis for their approaches. Therefore, a deep understanding of the mechanisms of functioning of such deep models is essential for discovering new architectures of neural networks. This paper proposes a smart city control system architecture based on deep convolutional neural networks. The control system has a multilayer architecture that combines loosely coupled intelligent components. As the main layer, a solution based on deep learning technology is applied, which allows solving several tasks simultaneously: segmentation, detection and classification of images received from surveillance cameras of the smart city system. The data obtained at the output of this layer is used for further analysis and decision-making in the smart city system. The proposed architecture has a high degree of modularity and allows the replacement of individual elements in a loosely coupled architecture. In this paper, deep learning and computer vision technologies are also considered, on the basis of which the image processing layer from video cameras is implemented. A masked recurrent neural network is used for this task.
Chemometric Analysis of a Ternary Mixture of Caffeine, Quinic Acid, and Nicotinic Acid by Terahertz Spectroscopy

Caffeine, quinic acid, and nicotinic acid are among the significant chemical determinants of coffee quality. This study develops a chemometric model to quantify these compounds in ternary mixtures analyzed by terahertz time-domain spectroscopy (THz-TDS). A data set of 480 THz spectra was obtained from 80 samples. Combinations of data preprocessing methods, including normalization (Z-score, min-max scaling, Mie baseline removal) and dimensionality reduction (principal component analysis (PCA), factor analysis (FA), independent component analysis (ICA), locally linear embedding (LLE), non-negative matrix factorization (NMF), isomap), and prediction models (partial least-squares regression (PLSR), support vector regression (SVR), multilayer perceptron (MLP), convolutional neural network (CNN), gradient boosting) were analyzed for their prediction performance (totaling to 4,711,685 combinations). Results show that the highest quantification performance was achieved at a root-mean-square error of prediction (RMSEP) of 0.0254 (dimensionless mass ratio), using min-max scaling and factor analysis for data preprocessing and multilayer perceptron for prediction. Effects of preprocessing, comparison of prediction models, and linearity of data are discussed.
Distributed Artificial Intelligence Enabled Aerial-Ground Networks: Architecture, Technologies and Challenges

Artificial intelligence (AI) provides a promising and novel direction to design future time-varying wireless networks by leading to significantly superior performances compared to conventional methods. In addition, the advanced deployment of unmanned aerial vehicles (UAVs) has boosted extensive novel research results and industrial products in terms of aerial-ground networks. However, with the rapid development of mobile networks and growing requirements for low-latency services, the conventional centralized aerial-ground network has failed to meet the time-varying expectations of mobile users in the dynamic network environment. To cope with the problems, the marriage of the aerial-ground network and innovative AI techniques, i.e., distributed artificial intelligence enabled aerial-ground network (DAIAGN), is proposed in this article, which consists of three vital components: deep reinforcement learning enabled distributed information sharing, edge intelligence enabled distributed security management, and multi-agent reinforcement learning enabled distributed decision making. The functions of the three components are elaborated, and recent related advances are surveyed in detail. A specific case study is also provided with respect to multi-agent reinforcement learning enabled distributed decision making. Furthermore, key challenges and open issues are also discussed to provide some guidances for potential future directions.
Building a Media Literate Society: Pathways to Improve Media and Information Literacy Education in Pakistan

This mixed-method study investigates the state of media and information literacy education in Pakistan by underlining the availability of relevant courses and trained teaching and research faculty at both public and private universities. Using United Nations Educational, Scientific and Cultural Organization’s (UNESCO) framework, this study explores the perception of faculty members across six key areas of general education and teacher development to improve the quality of media and information literacy in the country. These six key areas include (a) policy and vision, (b) curriculum and assessment, (c) pedagogy, (d) media and information, (e) organization and administration, and (f) teachers’ professional development.
Complexity Theory and its Applications in Linear Quantum Optics

This thesis is intended in part to summarize and also to contribute to the newest developments in passive linear optics that have resulted, directly or indirectly, from the somewhat shocking discovery in 2010 that the BosonSampling problem is likely hard for a classical computer to simulate. In doing so, I hope to provide a historic context for the original result, as well as an outlook on the future of technology derived from these newer developments. An emphasis is made in each section to provide a broader conceptual framework for understanding the consequences of each result in light of the others. This framework is intended to be comprehensible even without a deep understanding of the topics themselves. The first three chapters focus more closely on the BosonSampling result itself, seeking to understand the computational complexity aspects of passive linear optical networks, and what consequences this may have. Some effort is spent discussing a number of issues inherent in the BosonSampling problem that limit the scope of its applicability, and that are still active topics of research. Finally, we describe two other linear optical settings that inherit the same complexity as BosonSampling. The final chapters focus on how an intuitive understanding of BosonSampling has led to developments in optical metrology and other closely related fields. These developments suggest the exciting possibility that quantum sensors may be viable in the next few years with only marginal improvements in technology. Lastly, some open problems are presented which are intended to lay out a course for future research that would allow for a more complete picture of the scalability of the architecture developed in these chapters.
Physics of Mass Loss in Massive Stars

Abstract We review potential mass-loss mechanisms in the various evolutionary stages of massive stars, from the well-known line-driven winds of O-stars and BA-supergiants to the less-understood winds of Red Supergiants. We discuss optically thick winds from Wolf-Rayet stars and Very Massive Stars, and the hypothesis of porosity-moderated, continuum-driven mass loss from stars formally exceeding the Eddington limit, which might explain the giant outbursts from Luminous Blue Variables. We finish this review with a glance on the impact of rapid rotation, magnetic fields and small-scale inhomogeneities in line-driven winds.
Emotion recognition from facial images

This paper deals with the task of human emotion recognition in images. The authors analyzed and reviewed the results of other researchers in this task and provided a brief overview of the machine learning and deep learning methods used in the study. Dataset of images of people in seven different emotional states was selected for the experiments. The authors proposed various features for machine learning and deep learning models using information about the location of anthropometric points on a human face. Based on each of the proposed features, the authors created datasets on which they tested various machine learning methods and different architectures of perceptrons. The results of numerical experiments presented in the paper are classification metrics and confusion matrices received with considered models. The model that provides the highest f1 score was selected as the best model.
Multidimensionally constrained covariant density functional theories—nuclear shapes and potential energy surfaces

The intrinsic nuclear shapes deviating from a sphere not only manifest themselves in nuclear collective states but also play important roles in determining nuclear potential energy surfaces (PES’s) and fission barriers. In order to describe microscopically and self-consistently nuclear shapes and PES’s with as many shape degrees of freedom as possible included, we developed multidimensionally constrained covariant density functional theories (MDC-CDFTs). In MDC-CDFTs, the axial symmetry and the reflection symmetry are both broken and all deformations characterized by β λ μ with even μ are considered. We have used the MDC-CDFTs to study PES’s and fission barriers of actinides, the non-axial octupole Y32 correlations in N = 150 isotones and shapes of hypernuclei. In this Review we will give briefly the formalism of MDC-CDFTs and present the applications to normal nuclei.
Quantifying internal workload during training drills in handball players: comparison between heart rate and perceived exertion based methods

Aim: Monitoring internal workload is widely used in team sports. The association between the rating of perceived exertion-based (s-RPE) and heart rate (HR)-based methods needs further investigations regarding exercise modalities in handball. This study compared sRPE and HR-based responses during commonly used handball-training drills. Methods: Twelve professional male players were monitored in 6 separate occasions during two small-sided games (SSGs), two match-play periods (MPs) and two interval runs (IRs). The s-RPE and two HR-based methods were used to quantify workload. Pearson correlations were applied to determine relationships between quantification methods, and one-way ANOVA was used to compare workloads between exercise modalities for each method. Results: Nearly perfect correlation was observed (p < 0.0001, r = 0.93–0.99) between both HR-based methods for each exercise modality. Relationships between s-RPE and HR-based methods revealed trivial to small correlations. The perception of effort reported in response to SSGs and MPs sequences were much lower than IRs. Conclusion: s-RPE and HR-based methods do not seem to be interchangeable. They should be considered as complementary to understand psychophysiological workload. The s-RPE quantification of training drills might help coaches to better design their training session particularly during intense periods.
Experimental determination of the correlation properties of plasma turbulence using 2D BES systems

A procedure is presented to map from the spatial correlation parameters of a turbulent density field (the radial and binormal correlation lengths and wavenumbers, and the fluctuation amplitude) to correlation parameters that would be measured by a beam emission spectroscopy (BES) diagnostic. The inverse mapping is also derived, which results in resolution criteria for recovering correct correlation parameters, depending on the spatial response of the instrument quantified in terms of point-spread functions (PSFs). Thus, a procedure is presented that allows for a systematic comparison between theoretical predictions and experimental observations. This procedure is illustrated using the Mega-Ampere Spherical Tokamak BES system and the validity of the underlying assumptions is tested on fluctuating density fields generated by direct numerical simulations using the gyrokinetic code GS2. The measurement of the correlation time, by means of the cross-correlation time-delay method, is also investigated and is shown to be sensitive to the fluctuating radial component of velocity, as well as to small variations in the spatial properties of the PSFs.
CoRoT-22 b: a validated 4.9 R⊕ exoplanet in 10-d orbit

The CoRoT satellite has provided high-precision photometric light curves for more than 163,000 stars and found several hundreds of transiting systems compatible with a planetary scenario. If ground-based velocimetric observations are the best way to identify the actual planets among many possible configurations of eclipsing binary systems, recent transit surveys have shown that it is not always within reach of the radial-velocity detection limits. In this paper, we present a transiting exoplanet candidate discovered by CoRoT whose nature cannot be established from ground-based observations, and where extensive analyses are used to validate the planet scenario. They are based on observing constraints from radial-velocity spectroscopy, adaptive optics imaging and the CoRoT transit shape, as well as from priors on stellar populations, planet and multiple stellar systems frequency. We use the fully Bayesian approach developed in the PASTIS analysis software, and conclude that the planet scenario is at least 1400 times more probable than any other false positive scenario. The primary star is a metallic solar-like dwarf, with Ms = 1.099+-0.049 Msun and Rs = 1.136 (+0.038,-0.090) Rsun . The validated planet has a radius of Rp = 4.88 (+0.17,-0.39) RE and mass less than 49 ME. Its mean density is smaller than 2.56 g/cm^3 and orbital period is 9.7566+-0.0012 days. This object, called CoRoT-22 b, adds to a large number of validated Kepler planets. These planets do not have a proper measurement of the mass but allow statistical characterization of the exoplanet population.
Low Intensity Focused Ultrasound Ignited “Deep-Penetration Nanobomb” (DPNB) for Tetramodal Imaging Guided Hypoxia-Tolerant Sonodynamic Therapy Against Hypoxic Tumors

Background Sonodynamic therapy (SDT) has been regarded as a novel therapeutic modality for killing tumors. However, the hypoxic tumor microenvironment, especially deep-seated tumors distant from blood vessels, severely restricts therapeutic efficacy due to the oxygen-dependent manner of SDT. Methods Herein, we report a novel ultrasonic cavitation effect-based therapeutic modality that is able to facilitate the hypoxia-tolerant SDT for inducing hypoxic tumor death. A tLyP-1 functionalized liposomes is fabricated, composed of hematoporphyrin monomethyl ether gadolinium as the sonosentizer and perfluoropentane (PFP) as the acoustic environment regulator. Moreover, the tLyP-1 functioned liposomes could achieve active tumor homing and effective deep-penetrating into hypoxic tumors. Upon low intensity focused ultrasound (LIFU) irradiation, the acoustic droplet vaporization effect of PFP induced fast liquid-to-gas transition and quick bubbles explosion to generate hydroxyl radicals, efficiently promoting cell death in both normoxic and hypoxic microenvironment (acting as deep-penetration nanobomb, DPNB). Results The loading of PFP is proved to significantly enhance the therapeutic efficacy of hypoxic tumors. In particular, these DPNB can also act as ultrasound, photoacoustic, magnetic resonance, and near-infrared fluorescence tetramodal imaging agents for guiding the therapeutic process. Conclusion This study is the first report involving that liquid-to-gas transition based SDT has the potential to combat hypoxic tumors.
Mineralogy of a Possible Ancient Lakeshore in the Sutton Island Member of Mt. Sharp, Gale Crater, Mars, From Mastcam Multispectral Images

The Curiosity rover on the Mars Science Laboratory mission has found extensive evidence that Gale crater once hosted a habitable lacustrine environment; however, there are remaining questions about the chemistry and duration of the lake and the nature of the climate at the time. In this study, we use Mastcam multispectral data to investigate the mineralogy of the Sutton Island member of the Murray formation, a part of the basal layers of Mt. Sharp, which consists of heterolithic mudstone and sandstone that are distinct from the finely laminated mudstones that dominate much of the Murray. Sutton Island includes at least one instance of desiccation cracks, indicative of subaerial exposure, and uniquely irregular diagenetic features that may be related to local bedrock permeability. These features suggest that Sutton Island experienced a complex history of deposition and diagenesis which may be crucial for understanding changing water‐rock interactions within Gale. We find that most Mastcam bedrock spectra in this region lack the absorptions associated with hematite found throughout the Murray, and instead show deeper absorptions shifted toward longer wavelengths that are more consistent with Fe‐smectites such as nontronite. Elemental chemistry from ChemCam supports this interpretation, as SiO, MgO, Li, and the chemical index of alteration are elevated in this region. Combined with observations of bedrock sedimentology, this suggests that Sutton Island was deposited in a nearshore or low stand environment, and we hypothesize that the clay minerals were produced in this region due to sub‐aerial exposure and weathering in a semi‐arid climate.
Multiband optical variability studies of BL Lacertae

We monitored BL Lacertae for 13 nights in optical B, V, R, and I bands during October and November 2014 including quasi-simultaneous observations in V and R bands using two optical telescopes in India. We have studied multi-band optical flux variations, colour variation and spectral changes in this blazar. Source was found to be active during the whole monitoring period and showed significant intraday variability on 3 nights in V and R filters while displayed hints of variability on 6 other dates in R passband and on 2 nights in V filter. From the colour-magnitude analysis of the source we found that the spectra of the target gets flatter as it becomes brighter on intra-night timescale. Using discrete correlation technique, we found that intraday light curves in both V and R filters are almost consistent and well correlated with each other. We also generated spectral energy distribution (SED) of the target using the B, V, R, and I data sets for all 13 nights which could help us investigate the physical process responsible for the observed variations in BL Lacertae objects. We also discuss possible physical causes of the observed spectral variability.
CONSTRAINTS ON THE UBIQUITY OF CORONAL X-RAY CYCLES

Stellar activity cycles are known to be a widespread phenomenon amongst moderately active solar- and late-type stars from long-term periodic variations in chromospheric Ca ii H and K emission lines, yet to date, only a handful of coronal X-ray cycles are known. We have surveyed serendipitously observed stellar sources in fields observed multiple times in the last decade by XMM-Newton and present our analysis of nine stars from six fields. Since our sample is flux-limited, it is strongly biased toward higher levels of X-ray activity. We fit a single temperature apec spectrum to each source and search for significant periodicities using a Lomb–Scargle periodogram. We use a Monte Carlo (MC) algorithm to yield robust analysis of the statistical significance of cycle detections and non-detections. None of the nine stellar light curves show any convincing indications of periodicity. From MC simulations, we simulate the detection capabilities of our methodology and, assuming a uniform distribution of cycle periods and strengths over the domain searched, we conclude with 95% confidence that less than 72% of the stars represented by our sample of active stars have 5–13 year coronal X-ray cycles.
On the origins of C iv absorption profile diversity in broad absorption line quasars

There is a large diversity in the C IV broad absorption line (BAL) profile among BAL quasars (BALQs). We quantify this diversity by exploring the distribution of the C IV BAL properties, FWHM, maximum depth of absorption and its velocity shift ($v_{\rm md}$), using the SDSS DR7 quasar catalogue. We find the following: (i) Although the median C IV BAL profile in the quasar rest-frame becomes broader and shallower as the UV continuum slope ($\alpha_{\rm UV}$ at 1700-3000 A) gets bluer, the median individual profile in the absorber rest-frame remains identical, and is narrow (FWHM = 3500 km/s) and deep. Only 4 per cent of BALs have FWHM > 10,000 km/s. (ii) As the He II emission equivalent-width (EW) decreases, the distributions of FWHM and $v_{\rm md}$ extend to larger values, and the median maximum depth increases. These trends are consistent with theoretical models in which softer ionizing continua reduce overionization, and allow radiative acceleration of faster BAL outflows. (iii) As $\alpha_{\rm UV}$ becomes bluer, the distribution of $v_{\rm md}$ extends to larger values. This trend may imply faster outflows at higher latitudes above the accretion disc plane. (iv) For non-BALQs, the C IV emission line decreases with decreasing He II EW, and becomes more asymmetric and blueshifted. This suggests an increasing relative contribution of emission from the BAL outflow to the C IV emission line as the ionizing spectral energy distribution (SED) gets softer, which is consistent with the increasing fraction of BALQs as the ionizing SED gets softer.