catena-Poly[[diaqua­bis­(formato-κO)cobalt(II)]-μ2-2,6-bis­(pyridin-4-yl)-4,4′-bipyridine-κ2 N 2:N 6]

In the title complex, [Co(CHO2)2(C20H14N4)(H2O)2]n, the CoII ion, lying on an inversion center, is six-coordinated by two O atoms from two monodentate formate ligands, two N atoms from two 2,6-bis­(pyridin-4-yl)-4,4′-bipyridine (4-pybpy) ligands and two water mol­ecules, displaying an octa­hedral geometry. The 4-pybpy ligand, having a twofold rotation axis, functions in a bridging coordination mode, connecting the CoII ions into a corrugated chain along [01]. The chains are further linked into a three-dimensional supra­molecular network by O—H⋯O, C—H⋯N and C—H⋯O hydrogen bonds and π–π stacking inter­actions between the pyridine rings [centroid-to-centroid distance = 3.743 (2) Å]

A Comprehensive Analysis of Fermi Gamma-Ray Burst Data. IV. Spectral Lag and its Relation to E p Evolution

The spectral evolution and spectral lag behavior of 92 bright pulses from 84 gamma-ray bursts observed by the Fermi Gamma-ray Burst Monitor (GBM) telescope are studied. These pulses can be classified into hard-to-soft pulses (H2S; 64/92), H2S-dominated-tracking pulses (21/92), and other tracking pulses (7/92). We focus on the relationship between spectral evolution and spectral lags of H2S and H2S-dominated-tracking pulses. The main trend of spectral evolution (lag behavior) is estimated with ( ), where E p is the peak photon energy in the radiation spectrum, t + t 0 is the observer time relative to the beginning of pulse −t 0, and is the spectral lag of photons with energy E with respect to the energy band 8–25 keV. For H2S and H2S-dominated-tracking pulses, a weak correlation between and k E is found, where W is the pulse width. We also study the spectral lag behavior with peak time of pulses for 30 well-shaped pulses and estimate the main trend of the spectral lag behavior with . It is found that is correlated with k E . We perform simulations under a phenomenological model of spectral evolution, and find that these correlations are reproduced. We then conclude that spectral lags are closely related to spectral evolution within the pulse. The most natural explanation of these observations is that the emission is from the electrons in the same fluid unit at an emission site moving away from the central engine, as expected in the models invoking magnetic dissipation in a moderately high-σ outflow

A comprehensive analysis of Fermi Gamma-Ray Burst Data: IV. Spectral lag and Its Relation to Ep Evolution

The spectral evolution and spectral lag behavior of 92 bright pulses from 84 gamma-ray bursts (GRBs) observed by the Fermi GBM telescope are studied. These pulses can be classified into hard-to-soft pulses (H2S, 64/92), H2S-dominated-tracking pulses (21/92), and other tracking pulses (7/92). We focus on the relationship between spectral evolution and spectral lags of H2S and H2S-dominated-tracking pulses. %in hard-to-soft pulses (H2S, 64/92) and H2S-dominating-tracking (21/92) pulses. The main trend of spectral evolution (lag behavior) is estimated with $\log E_p\propto k_E\log(t+t_0)$ (${\hat{\tau}} \propto k_{\hat{\tau}}\log E$), where $E_p$ is the peak photon energy in the radiation spectrum, $t+t_0$ is the observer time relative to the beginning of pulse $-t_0$, and ${\hat{\tau}}$ is the spectral lag of photons with energy $E$ with respect to the energy band $8$-$25$ keV. For H2S and H2S-dominated-tracking pulses, a weak correlation between $k_{{\hat{\tau}}}/W$ and $k_E$ is found, where $W$ is the pulse width. We also study the spectral lag behavior with peak time $t_{\rm p_E}$ of pulses for 30 well-shaped pulses and estimate the main trend of the spectral lag behavior with $\log t_{\rm p_E}\propto k_{t_p}\log E$. It is found that $k_{t_p}$ is correlated with $k_E$. We perform simulations under a phenomenological model of spectral evolution, and find that these correlations are reproduced. We then conclude that spectral lags are closely related to spectral evolution within the pulse. The most natural explanation of these observations is that the emission is from the electrons in the same fluid unit at an emission site moving away from the central engine, as expected in the models invoking magnetic dissipation in a moderately-high-$\sigma$ outflow.Comment: 58 pages, 11 figures, 3 tables. ApJ in pres

Rewards and errors in multi-arm bandits for interactive education

International audienceIn multi-armed bandits, the most common objective is the maximization of the cumulative reward. Alternative settings include active exploration, where a learner tries to gain accurate estimates of the rewards of all arms. While these objectives are contrasting, in many scenarios it is desirable to trade off rewards and errors. For instance, in educational games the designer wants to gather generalizable knowledge about the behavior of the students and teaching strategies (small estimation errors) but, at the same time, the system needs to avoid giving a bad experience to the players, who may leave the system permanently (large reward). In this paper, we formalize this tradeoff and introduce the ForcingBalance algorithm whose performance is provably close to the best possible tradeoff strategy. Finally, we demonstrate on real-world educational data that ForcingBalance returns useful information about the arms without compromising the overall reward

Trading off rewards and errors in multi-armed bandits

International audienceIn multi-armed bandits, the most common objective is the maximization of the cumulative reward. Alternative settings include active exploration, where a learner tries to gain accurate estimates of the rewards of all arms. While these objectives are contrasting, in many scenarios it is desirable to trade off rewards and errors. For instance, in educational games the designer wants to gather generalizable knowledge about the behavior of the students and teaching strategies (small estimation errors) but, at the same time, the system needs to avoid giving a bad experience to the players, who may leave the system permanently (large reward). In this paper, we formalize this tradeoff and introduce the ForcingBalance algorithm whose performance is provably close to the best possible tradeoff strategy. Finally, we demonstrate on real-world educational data that ForcingBalance returns useful information about the arms without compromising the overall reward

Evaluation of several adjuvants in avian influenza vaccine to chickens and ducks

The effects of three different adjuvants, mineral oil, Montanide™ ISA 70M VG, and Montanide™ ISA 206 VG, were evaluated on reverse genetics H5N3 avian influenza virus cell cultured vaccine. The immune results of SPF chickens after challenging with highly pathogenic avian influenza (HPAI) virus demonstrated that mineral oil adjuvant group and 70M adjuvant group provided 100% protection efficiency, but 206 adjuvant group provided only 40%. Statistical analysis indicated that the protection effects of mineral oil adjuvant group and the 70M adjuvant showed no significant difference to each other, but with significant difference to 206 adjuvant group. All three groups could induce high titres of antibody after immunizing SPF ducks, but there was no significant difference among them. The immunization effect of 70M adjuvant group on SPF chickens were the best and showed significant difference compared with optimized 70Mi Montanide™ eight series adjuvants groups. These results suggest that 70M adjuvant could be a novel adjuvant for preparing avian influenza vaccine