Complexity-entropy analysis at different levels of organization in written language

Written language is complex. A written text can be considered an attempt to convey a meaningful message which ends up being constrained by language rules, context dependence and highly redundant in its use of resources. Despite all these constraints, unpredictability is an essential element of natural language. Here we present the use of entropic measures to assert the balance between predictability and surprise in written text. In short, it is possible to measure innovation and context preservation in a document. It is shown that this can also be done at the different levels of organization of a text. The type of analysis presented is reasonably general, and can also be used to analyze the same balance in other complex messages such as DNA, where a hierarchy of organizational levels are known to exist

Weak-lensing analysis of galaxy pairs using CS82 data

Here we analyze a sample of close galaxy pairs (relative projected separation < 25 h -1 kpc and relative radial velocities < 350 km s -1 ) using a weak-lensing analysis based on the Canada-France-Hawaii Telescope Stripe 82 Survey (CS82). We determine halo masses for the total sample of pairs as well as for interacting, red, and higher-luminosity pair subsamples with ∼3σ confidence. The derived lensing signal for the total sample can be fitted either by a Singular Isothermal Sphere (SIS) with σ V = 223 ± 24 km s -1 or a Navarro-Frenk-White (NFW) profile with R 200 = 0.30 ± 0.03 h -1 Mpc. The pair total masses and total r band luminosities imply an average mass-to-light ratio of ∼200 h M ⊙ /L ⊙ . On the other hand, red pairs which include a larger fraction of elliptical galaxies, show a larger mass-to-light ratio of ∼345 h M ⊙ /L ⊙ . Derived lensing masses were compared to a proxy of the dynamical mass, obtaining a good correlation. However, there is a large discrepancy between lensing masses and the dynamical mass estimates, which could be accounted for by astrophysical processes such as dynamical friction, by the inclusion of unbound pairs, and by significant deviations of the density distribution from SIS and NFW profiles in the inner regions. We also compared lensing masses with group mass estimates, finding very good agreement with the sample of groups with two members. Red and blue pairs show large differences between group and lensing masses, which is likely due to the single mass-to-light ratio adopted to compute the group masses.Fil: Gonzalez, Elizabeth Johana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba; ArgentinaFil: Rodriguez, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba; ArgentinaFil: Garcia Lambas, Diego Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba; ArgentinaFil: Makler, Martín. Centro Brasileiro de Pesquisas Físicas; BrasilFil: Mesa, Valeria Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Alonso, Sol. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales. Departamento de Geofísica y Astronomía; ArgentinaFil: Duplancic Videla, Maria Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales. Departamento de Geofísica y Astronomía; ArgentinaFil: Pereira, Maria E. S.. Brandeis University; Estados UnidosFil: Shan, HuanYuan. Shanghai Astronomical Observatory; República de Chin

Composición química del aceite esencial de las hojas de Cymbopogon nardus y Cymbopogon citratus

El aceite esencial de hojas de Cymbopogon nardus y Cymbopogon citratus fue obtenido por destilación por arrastre con vapor de agua y analizado por cromatografía de gases de alta resolución (CGAR) y cromatografía de gases de alta resolución acoplado a espectrometría de masas (CGAR-EM) la composición química del aceite esencial de las partes aéreas de Citronela (Cymbopogon nardus) y Limoncillo (Cymbopogon citratus) cultivados en Caquetá. En el aceite esencial de C. nardus predominó citronelal (26%), geraniol (14%), elemol (9.9%), germacreno D-4-ol (6.5), citronelol (6.1%) y acetato de geranilo (5.6%), mientras que en el de C. citratos predominó geranial (49.7%), neral (30.5%) y â-mirceno (12.2%). El aceite esencial de C. citratus es menos denso y más ácido que el de C. nardus, en tanto que ambos aceites son dextro-rotatorios. De otro lado, se registran los espectros IR y UV de ambos aceites esenciales

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

Threat of allergenic airborne grass pollen in Szczecin, NW Poland: the dynamics of pollen seasons, effect of meteorological variables and air pollution

The dynamics of Poaceae pollen season, in particularly that of the Secale genus, in Szczecin (western Poland) 2004–2008 was analysed to establish a relationship between the meteorological variables, air pollution and the pollen count of the taxa studied. Consecutive phases during the pollen season were defined for each taxon (1, 2.5, 5, 25, 50, 75, 95, 97.5, 99% of annual total), and duration of the season was determined using the 98% method. On the basis of this analysis, the temporary differences in the dynamics of the seasons were most evident for Secale in 2005 and 2006 with the longest main pollen season (90% total pollen). The pollen season of Poaceae started the earliest in 2007, when thermal conditions were the most favourable. Correlation analysis with meteorological factors demonstrated that the relative humidity, mean and maximum air temperature, and rainfall were the factors influencing the average daily pollen concentrations in the atmosphere; also, the presence of air pollutants such as ozone, PM10 and SO2 was statistically related to the pollen count in the air. However, multiple regression models explained little part of the total variance. Atmospheric pollution induces aggravation of symptoms of grass pollen allergy

Measurement of the Forward-Backward Asymmetry in the B -> K(*) mu+ mu- Decay and First Observation of the Bs -> phi mu+ mu- Decay

We reconstruct the rare decays $B^+ \to K^+\mu^+\mu^-$, $B^0 \to K^{*}(892)^0\mu^+\mu^-$, and $B^0_s \to \phi(1020)\mu^+\mu^-$ in a data sample corresponding to $4.4 {\rm fb^{-1}}$ collected in $p\bar{p}$ collisions at $\sqrt{s}=1.96 {\rm TeV}$ by the CDF II detector at the Fermilab Tevatron Collider. Using $121 \pm 16$ $B^+ \to K^+\mu^+\mu^-$ and $101 \pm 12$ $B^0 \to K^{*0}\mu^+\mu^-$ decays we report the branching ratios. In addition, we report the measurement of the differential branching ratio and the muon forward-backward asymmetry in the $B^+$ and $B^0$ decay modes, and the $K^{*0}$ longitudinal polarization in the $B^0$ decay mode with respect to the squared dimuon mass. These are consistent with the theoretical prediction from the standard model, and most recent determinations from other experiments and of comparable accuracy. We also report the first observation of the $B^0_s \to \phi\mu^+\mu^- decay and measure its branching ratio${\mathcal{B}}(B^0_s \to \phi\mu^+\mu^-) = [1.44 \pm 0.33 \pm 0.46] \times 10^{-6}$using$27 \pm 6$signal events. This is currently the most rare$B^0_s$ decay observed.Comment: 7 pages, 2 figures, 3 tables. Submitted to Phys. Rev. Let

Search for a New Heavy Gauge Boson Wprime with Electron + missing ET Event Signature in ppbar collisions at sqrt(s)=1.96 TeV

We present a search for a new heavy charged vector boson $W^\prime$ decaying to an electron-neutrino pair in $p\bar{p}$ collisions at a center-of-mass energy of 1.96\unit{TeV}. The data were collected with the CDF II detector and correspond to an integrated luminosity of 5.3\unit{fb}^{-1}. No significant excess above the standard model expectation is observed and we set upper limits on $\sigma\cdot{\cal B}(W^\prime\to e\nu)$. Assuming standard model couplings to fermions and the neutrino from the $W^\prime$ boson decay to be light, we exclude a $W^\prime$ boson with mass less than 1.12\unit{TeV/}c^2 at the 95\unit{%} confidence level.Comment: 7 pages, 2 figures Submitted to PR

Measurements of the properties of Lambda_c(2595), Lambda_c(2625), Sigma_c(2455), and Sigma_c(2520) baryons

We report measurements of the resonance properties of Lambda_c(2595)+ and Lambda_c(2625)+ baryons in their decays to Lambda_c+ pi+ pi- as well as Sigma_c(2455)++,0 and Sigma_c(2520)++,0 baryons in their decays to Lambda_c+ pi+/- final states. These measurements are performed using data corresponding to 5.2/fb of integrated luminosity from ppbar collisions at sqrt(s) = 1.96 TeV, collected with the CDF II detector at the Fermilab Tevatron. Exploiting the largest available charmed baryon sample, we measure masses and decay widths with uncertainties comparable to the world averages for Sigma_c states, and significantly smaller uncertainties than the world averages for excited Lambda_c+ states.Comment: added one reference and one table, changed order of figures, 17 pages, 15 figure

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications