Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Anticoccidials on Oxidative Stress , Production Performance and Faecal Oocyst Counts in Broiler Chickens Infected with Eimeria Species

The influence of certain anticoccidial drugs on oxidative stress in broiler chickens infected with Eimeria species was assessed. There were two untreated (uninfected and infected), and three groups infected and treated with anticoccidials. The first treated group (Ro) was given robenidine, the 2nd a herbal anticoccidial (Herb) and the 3rd the combination of robenidine and the herbal anticoccidial (Ro+Herb). All infected groups were on day 14 challenged with oral inoculation of oocysts. The activities of catalase (CAT), superoxide dismutase (SOD) and glutathione S-transferase (GST), and the concentration of malondialdehyde (MDA) were estimated in blood taken on days 21 and 40. The oocyst numbers were calculated per gram, and chicken body weight and feed conversion ratio (FCR) measured. The activities of CAT, GST and the level of MDA were significantly lower (P<0.05), whilst the activity of SOD was higher in infected chickens treated with anticoccidials (P<0.05) in comparison to those untreated. The most prominent change in the parameters of oxidative stress was recorded in the Ro+Herb group. In chickens treated with anticoccidials body weight was significantly higher (P<0.05), and the FCR and the oocyst counts significantly lower (P<0.05) than in untreated chickens. Oocyst counts were lower in the Ro and Ro+Herb groups than in the Herb group. Our study demonstrated that both anticoccidial substances exerted antioxidant and anticoccidial effects.
The Red Supergiant Content of M31 and M33

We identify red supergiants (RSGs) in our spiral neighbors M31 and M33 using near-IR (NIR) photometry complete to a luminosity limit of . Our archival survey data cover 5 deg2 of M31, and 3 deg2 for M33, and are likely spatially complete for these massive stars. Gaia is used to remove foreground stars, after which the RSGs can be separated from asymptotic giant branch (AGB) stars in the color–magnitude diagram. The photometry is used to derive effective temperatures and bolometric luminosities via MARCS stellar atmosphere models. The resulting H-R diagrams show superb agreement with the evolutionary tracks of the Geneva evolutionary group. Our census includes 6400 RSGs in M31 and 2850 RSGs in M33 within their Holmberg radii; by contrast, only a few hundred RSGs are known so far in the Milky Way. Our catalog serves as the basis for a study of the RSG binary frequency being published separately, as well as future studies relating to the evolution of massive stars. Here we use the matches between the NIR-selected RSGs and their optical counterparts to show that the apparent similarity in the reddening of OB stars in M31 and M33 is the result of Malmquist bias; the average extinction in M31 is likely higher than that of M33. As expected, the distribution of RSGs follows that of the spiral arms, while the much older AGB population is more uniformly spread across each galaxy’s disk.
Household Food Insecurity And Iron Deficiency Anemia In Mexican Women Of Reproductive

30" Iron Deficiency Anemia (IDA) is a major cause of maternal mortality. Our objective was 31" to investigate the association of household food insecurity (HFI) with IDA in a nationally 32" representative, cross-sectional sample from Mexico of women of reproductive age (12-49 years 33" old). We tested the association between HFI and IDA among 16,944 women of reproductive age 34" using multiple logistic regression. HFI was measured using the Latin America and the 35" Caribbean Food Security Scale (ELCSA). IDA was measured with capillary hemoglobin using 36" HemoCue photometer and defined using WHO standards. Multivariate analyses showed that 37" adjusted odds for IDA were 33% and 36% higher among women living in moderately and 38" severely food insecure households (vs. food secure households), respectively. We conclude that 39" HFI is a risk factor for IDA. Reducing HFI may be an effective strategy to reduce the risk of IDA 40" among Mexican women. 41" 42"
A low [CII]/[NII] ratio in the center of a massive galaxy at z = 3.7: evidence for a transition to quiescence at high redshift

Understanding the process of quenching is one of the major open questions in galaxy evolution and crucial insights may be obtained by studying quenched galaxies at high redshifts at epochs when the Universe and the galaxies were younger and simpler to model. However, establishing the degree of quiescence in high-redshift galaxies is a challenging task. One notable example is Hyde, a recently discovered galaxy at zspec = 3.709. Equally as compact (r1/2 ∼ 0.5 kpc) and massive (M* ∼ 1011 M⊙) as its quenched neighbor Jekyll, it is also extremely obscured yet only moderately luminous in the sub-millimeter. Panchromatic modeling has suggested it could be the first galaxy found in transition to quenching at z > 3, however, the data are also consistent with a broad range of star-formation activity, from fully quenched to moderate star-formation rates (SFR) in the lower scatter of the galaxy main-sequence. Here, we describe Atacama Large Millimeter Array observations of the [C II] 157 μm and [N II] 205 μm far-infrared emission lines. The [C II] emission within the half-light radius is dominated by ionized gas, while the outskirts are dominated by photo-dissociation regions or neutral gas. This suggests that the ionization in the center is not primarily powered by ongoing star formation, and is instead coming from remnant stellar populations formed in an older burst or from a moderate active galactic nucleus . Accounting for this information in the multi-wavelength modeling provides a tighter constraint on the star formation rate of SFR = 50−18+24 M⊙ yr−1 yr−1. This rules out fully quenched solutions and favors SFRs more than factor of two lower than expected for a main-sequence galaxy, confirming the nature of Hyde as a transition galaxy. These results suggest that quenching happens from inside-out and starts before the galaxy expels or consumes all its gas reservoirs. Similar observations of a sample of massive and obscured galaxies would determine whether this is an isolated case or the norm for quenching at high redshift.
DEEP REINFORCEMENT LEARNING FOR AUTONOMOUS VEHICLES - STATE OF THE ART

: Reinforcement learning is considered to be one of the strongest paradigms in AI domain, which can be applied to teach machines how to behave through environment interaction. Recently the concept of deep reinforcement learning (DRL) was introduced and was tested with success in games like Atari 2600 or Go, proving the capability to learn a good representation of the environment. In this moment there are few implementations of DRL in the autonomous driving field. In this paper, we present the state of the art in deep reinforcement learning paradigm highlighting the current achievements for autonomous driving vehicles.
DNN Intellectual Property Protection: Taxonomy, Methods, Attack Resistance, and Evaluations

The training and creation of deep learning model is usually costly, thus it can be regarded as an intellectual property (IP) of the model creator. However, malicious users who obtain high-performance models may illegally copy, redistribute, abuse the models, or use the models to provide prediction services without permission. To deal with such security threats, a few deep neural networks (DNN) IP protection methods have been proposed in recent years. This paper attempts to provide a review of the existing DNN IP protection works and also an outlook. First, we propose the first taxonomy for DNN IP protection methods in terms of five attributes: scenario, capacity, type, mechanism, and attack resistance. Second, we present a survey on existing DNN IP protection works in terms of the above five attributes, especially focusing on the challenges these methods face, whether these methods can provide proactive protection, and their resistances to different levels of attacks. Third, we analyze the potential attacks on DNN IP protection methods. Fourth, we propose a systematic evaluation method for DNN IP protection methods. Lastly, challenges and future works are presented.
Causal Version of Newtonian Theory by Time – Retardation of the Gravitational Field Explains the Flyby Anomalies

Classical Newtonian gravitational theory does not satisfy he causality principle because it is based on instantaneous action-at-a-distance. A causa l version of Newtonian theory for a large rotating sphere is derived herein by time-retard ing the distance between interior circulating point-mass sources and an exterior fie ld-point. The resulting causal theory explains exactly the six flyby anomalies reported by A nderson et al. in 2008. It also explains exactly an anomalous decrease in the Moon’s orbital speed. No other known theory has been shown to explain both the flyby anomalie s nd the lunar orbit anomaly.
Multiscale Probing of Colloidal Gelation Dynamics

Colloidal gels are viscoelastic materials characterized by the collective behavior of particles that form a space-spanning network. Although the network structure embodies the aggregation process of the particles, the kinetic pathway from a stable suspension to such a complex microstructure remains poorly understood. In this work, we explore the evolution of microscopic structure and dynamics of home-made colloidal particles in the early phase of gelation, by extending the applicability of Differential Dynamic Microscopy (DDM) to non-ergodic media. We demonstrate uncoupled development of the structure and dynamics that reveals an intermediate stage of gel formation, and compare the DDM results with the rheological features of evolving gels. We finally show how understanding the gelation at multiple length and time scales via DDM and rheology opens new ways to tune the mechanical properties of colloidal gels that bear inherent versatility. Thesis Supervisor: Irmgard Bischofberger Title: Assistant Professor of Mechanical Engineering
Selected Issues from the Dark Side of the General Data Protection Regulation

Abstract The Regulation (EU) 2016/679 on the protection of personal data (GDPR) was enacted in 2016 and applies from 25thMay 2018 in the entire EU. The GDPR is a product of an ambitious reform and represents a direct penetration of the EU law into the legal systems of the EU member states. The EU works on the enhancement of awareness about the GDPR and points out its bright side. However, the GDPR has its dark side as well, which will inevitably have a negative impact. Hence, the goal of this paper is twofold - (i) to scientifically identify, forecast, and analyze selected problematic aspects of the GDPR and its implementation, in particular for Czech municipalities, and (ii) to propose recommendations about how to reduce, or even avoid, their negative impacts. These theoretic analyses are projected to a Czech case study focusing on municipalities, which offers fresh primary data and allows a further refining of the proposed recommendations. An integral part of the performed analyses is also a theoretic forecast of expenses linked to the GDPR, which municipalities will have to include in their mandatory expenses and mid-term prognostic expectations regarding the impact on the budgets of these municipalities from Central Bohemia. The GDPR, like Charon, is at the crossing, the capacity and knowledge regarding its application is critical for operating in the EU in 2018. It is time both to admit that the GDPR has its dark side and to present real and practical recommendations about how to mitigate it.
CHARACTERISTIC POLYNOMIALS OF SIMPLE ORDINARY ABELIAN VARIETIES OVER FINITE FIELDS

Abstract We provide an easy method for the construction of characteristic polynomials of simple ordinary abelian varieties ${{\mathcal A}}$ of dimension g over a finite field ${{\mathbb F}}_q$ , when $q\ge 4$ and $2g=\rho ^{b-1}(\rho -1)$ , for some prime $\rho \ge 5$ with $b\ge 1$ . Moreover, we show that ${{\mathcal A}}$ is absolutely simple if $b=1$ and g is prime, but ${{\mathcal A}}$ is not absolutely simple for any prime $\rho \ge 5$ with $b>1$ .
Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytope

Bayesian optimization is a popular method for solving the problem of global optimization of an expensive-to-evaluate black-box function. It relies on a probabilistic surrogate model of the objective function, upon which an acquisition function is built to determine where next to evaluate the objective function. In general, Bayesian optimization with Gaussian process regression operates on a continuous space. When input variables are categorical or discrete, an extra care is needed. A common approach is to use one-hot encoded or Boolean representation for categorical variables which might yield a {\em combinatorial explosion} problem. In this paper we present a method for Bayesian optimization in a combinatorial space, which can operate well in a large combinatorial space. The main idea is to use a random mapping which embeds the combinatorial space into a convex polytope in a continuous space, on which all essential process is performed to determine a solution to the black-box optimization in the combinatorial space. We describe our {\em combinatorial Bayesian optimization} algorithm and present its regret analysis. Numerical experiments demonstrate that our method outperforms existing methods.
keletal muscle functional and structural adaptations after eccentric verload flywheel resistance training : a systematic review and eta-analysis

Objectives: The purpose of this meta-analysis was to examine the effect of flywheel (FW) resistance training with Eccentric Overload (FW-EOT) on muscle size and functional capacities (i.e. strength and power) in athletes and healthy subjects, and to compare FW-induced adaptations with those triggered by traditional resistance exercise interventions. Design: A systematic review and meta-analysis of randomised controlled trials. Methods: A search of electronic databases [PubMed, MEDLINE (SportDiscus), Web of Science, Scopus and PEDro] was conducted to identify all publications employing FW-EOT up to April 30, 2016. Outcomes were analyzed as continuous outcomes using a random effects model to calculate a standardized mean difference (SMD) and 95% CI. A total of 9 studies with 276 subjects and 92 effect sizes met the inclusion criteria and were included in the statistical analyses. Results: The overall pooled estimate from the main effects analysis was 0.63 (95% CI 0.49–0.76) with a significant (p < 0.001) Z overall effect of 9.17. No significant heterogeneity (p value = 0.78) was found. The meta-analysis showed significant differences between FW-EOT vs. conventional resistance training in concentric and eccentric strength, muscle power, muscle hypertrophy, vertical jump height and running speed, favoring FW-EOT. Conclusions: This meta-analysis provides evidence supporting the superiority of FW-EOT, compared with traditional weight-stack exercise, to promote skeletal muscle adaptations in terms of strength, power and size in healthy subjects and athletes. © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
Robust orbital stabilization: A Floquet theory–based approach

The design of robust orbitally stabilizing feedback is considered. From a known orbitally stabilizing controller for a nominal, disturbance‐free system, a robustifying feedback extension is designed utilizing the sliding‐mode control (SMC) methodology. The main contribution of the article is to provide a constructive procedure for designing the time‐invariant switching function used in the SMC synthesis. More specifically, its zero‐level set (the sliding manifold) is designed using a real Floquet–Lyapunov transformation to locally correspond to an invariant subspace of the Monodromy matrix of a transverse linearization. This ensures asymptotic stability of the periodic orbit when the system is confined to the sliding manifold, despite any system uncertainties and external disturbances satisfying a matching condition. The challenging task of oscillation control of the underactuated cart–pendulum system subject to both matched‐ and unmatched disturbances/uncertainties demonstrates the efficacy of the proposed scheme.
Spatial Component-wise Convolutional Network (SCCNet) for Motor-Imagery EEG Classification

We study brain-computer interfaces (BCI) based on the decoding of motor imagery (MI) from electroencephalography (EEG) neuromonitoring. The robustness of MI-BCI is a major concern in practical applications, and hence various efforts in the literature have been made to enhance the MI classification accuracy from EEG signals. Recently, classifiers based on convolutional neural networks (CNN) have achieved state-of-the-art performance. In further exploration of applying CNNs to EEG data, we propose a spatial component-wise convolutional network (SCCNet), featuring an initial convolutional layer for spatial filtering, a common processing in EEG analysis for signal enhancement and noise reduction. Through a series of optimization and validation, we show the superiority of SCCNet in MI EEG classification, outperforming other existing CNNs.
Outlier Detection in Nonparametric Frontier Models

In nonparametric frontier models, it is clear that outlying points can have a large influence on the efficiency of production units. The influence will however depend on the type of distance that is used to benchmark the production unit under analysis. The directional distance function measures the distance from a production unit to its full frontier along a linear path for a given direction while its robust counterpart considers its distance to the so-called order-α partial frontier. The first contribution is to study the robustness properties of this order-α directional efficiency estimator through the concept of influence function and breakdown point. To do so, the explicit link between the hyperbolic and the directional types of orientation is used. The second contribution is to propose a methodology using an adapted kernel density estimator to identify influential points with the directional efficiency estimator. This technique considers the density estimation of a transformed variable that depends on both the production unit being analysed as well as the direction used to benchmark its efficiency. To illustrate the results, some numerical examples are given as well as a real life example on the research efficiency of US universities.
Top quark pair production in ATLAS

The Large Hadron Collider (LHC) is a top quark factory. Since March 2010, the ATLAS detector is recording data from proton-proton collisions at the LHC at the unprecedented centre of mass energy of 7 TeV. After QCD jets, W and Z bosons, the production of top quarks is the dominant process. The search for first top pairs candidate events and the related background studies using data-driven techniques are here reported using an integrated luminosity of 295 nb. Several candidate events are observed, in both semileptonic and di-leptonic channels. The properties of these events are described and compared to the expectations from Monte Carlo simulation.
Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems

Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make such intelligent systems transparent and explainable. The need for explainable AI does not stem only from ethical and moral grounds but also from stricter legislation around the world mandating clear and justifiable explanations of any decision taken or assisted by AI. Especially in the medical context where Computer-Aided Diagnosis can have a direct influence on the treatment and well-being of patients, transparency is of utmost importance for safe transition from lab research to real world clinical practice. This paper provides a comprehensive overview of current state-of-the-art in explaining and interpreting Deep Learning based algorithms in applications of medical research and diagnosis of diseases. We discuss early achievements in development of explainable AI for validation of known disease criteria, exploration of new potential biomarkers, as well as methods for the subsequent correction of AI models. Various explanation methods like visual, textual, post-hoc, ante-hoc, local and global have been thoroughly and critically analyzed. Subsequently, we also highlight some of the remaining challenges that stand in the way of practical applications of AI as a clinical decision support tool and provide recommendations for the direction of future research.
Fast and easy super-sample covariance of large-scale structure observables

We present a numerically cheap approximation to super-sample covariance (SSC) of large-scale structure cosmological probes, first in the case of angular power spectra. No new elements are needed besides those used to predict the considered probes, thus relieving analysis pipelines from having to develop a full SSC modeling, and reducing the computational load. The approximation is asymptotically exact for fine redshift bins Δz → 0. We furthermore show how it can be implemented at the level of a Gaussian likelihood or a Fisher matrix forecast as a fast correction to the Gaussian case without needing to build large covariance matrices. Numerical application to a Euclid-like survey show that, compared to a full SSC computation, the approximation nicely recovers the signal-to-noise ratio and the Fisher forecasts on cosmological parameters of the wCDM cosmological model. Moreover, it allows for a fast prediction of which parameters are going to be the most affected by SSC and at what level. In the case of photometric galaxy clustering with Euclid-like specifications, we find that σ8, ns, and the dark energy equation of state w are particularly heavily affected. We finally show how to generalize the approximation for probes other than angular spectra (correlation functions, number counts, and bispectra) and at the likelihood level, allowing for the latter to be non-Gaussian if necessary. We release publicly a Python module allowing the implementation of the SSC approximation and a notebook reproducing the plots of the article.
Springtime CO 2 exchange over seasonal sea ice in the Canadian Arctic Archipelago

Springtime measurements of CO2 exchange over seasonal sea ice in the Canadian Arctic Archipelago using eddy covariance show that CO2 was generally released to the atmosphere during the cold (ice surface temperatures less than about –68C) early part of the season, but was absorbed from the atmosphere as warming advanced. Hourly maximum efflux and uptake rates approached 1.0 and –3.0mmolm s, respectively. These CO2 flux rates are far greater than previously reported over sea ice and are comparable in magnitude to exchanges observed within other systems (terrestrial and marine). Uptake generally occurred for wind speeds in excess of 6m s and corresponded to local maxima in temperature at the snow–ice interface and net radiation. Efflux, on the other hand, occurred under weaker wind speeds and periods of local minima in temperature and net radiation. The wind speeds associated with uptake are above a critical threshold for drifting and blowing snow, suggesting that ventilation of the snowpack and turbulent exchange with the brine-wetted grains are an important part of the process. Both the uptake and release fluxes may be at least partially driven by the temperature sensitivity of the carbonate system speciation in the brine-wetted snow base and upper sea ice. The period of maximum springtime CO2 uptake occurred as the sea-ice permeability increased, passing a critical threshold allowing vertical brine movement throughout the sea-ice sheet. At this point, atmospheric CO2 would have been available to the under-ice sea-water carbonate system, with ramifications for carbon cycling in sea-ice-dominated polar waters.