Individual and Domain Adaptation in Sentence Planning for Dialogue

One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations

Model–data comparison and data assimilation of mid-Holocene Arctic sea ice concentration

The consistency between new quantitative reconstructions of Arctic sea ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The response of the models mainly follows the increase in summer insolation, modulated to a limited extent by changes in atmospheric circulation. This leads to differences between regions in the models that are smaller than in the reconstruction. It is, however, impossible to precisely assess the models' skills because the sea ice concentration changes at the mid-Holocene are small in both the reconstructions and the models and of the same order of magnitude as the reconstruction uncertainty. Performing simulations with data assimilation using the model LOVECLIM amplifies the regional differences and improves the model–data agreement as expected. This is mainly achieved through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin, inducing an increase in the ice concentration in the Barents and Chukchi seas. This underlines the potential role of atmospheric circulation in explaining the reconstructed changes during the Holocene

Near-threshold high-order harmonic spectroscopy with aligned molecules

We study high-order harmonic generation in aligned molecules close to the ionization threshold. Two distinct contributions to the harmonic signal are observed, which show very different responses to molecular alignment and ellipticity of the driving field. We perform a classical electron trajectory analysis, taking into account the significant influence of the Coulomb potential on the strong-field-driven electron dynamics. The two contributions are related to primary ionization and excitation processes, offering a deeper understanding of the origin of high harmonics near the ionization threshold. This work shows that high harmonic spectroscopy can be extended to the near-threshold spectral range, which is in general spectroscopically rich.Comment: 4 pages, 4 figure

High-harmonic generation: taking control of polarization

The ability to control the polarization of short-wavelength radiation generated by high-harmonic generation is useful not only for applications but also for testing conservation laws in physics

Investigating the consistency between proxy-based reconstructions and climate models using data assimilation: a mid-Holocene case study

The mid-Holocene (6 kyr BP; thousand years before present) is a key period to study the consistency between model results and proxy-based reconstruction data as it corresponds to a standard test for models and a reasonable number of proxy-based records is available. Taking advantage of this relatively large amount of information, we have compared a compilation of 50 air and sea surface temperature reconstructions with the results of three simulations performed with general circulation models and one carried out with LOVECLIM, a model of intermediate complexity. The conclusions derived from this analysis confirm that models and data agree on the large-scale spatial pattern but the models underestimate the magnitude of some observed changes and that large discrepancies are observed at the local scale. To further investigate the origin of those inconsistencies, we have constrained LOVECLIM to follow the signal recorded by the proxies selected in the compilation using a data-assimilation method based on a particle filter. In one simulation, all the 50 proxy-based records are used while in the other two only the continental or oceanic proxy-based records constrain the model results. As expected, data assimilation leads to improving the consistency between model results and the reconstructions. In particular, this is achieved in a robust way in all the experiments through a strengthening of the westerlies at midlatitude that warms up northern Europe. Furthermore, the comparison of the LOVECLIM simulations with and without data assimilation has also objectively identified 16 proxy-based paleoclimate records whose reconstructed signal is either incompatible with the signal recorded by some other proxy-based records or with model physics

Sum of exit times in series of metastable states in probabilistic cellular automata

Reversible Probabilistic Cellular Automata are a special class of automata whose stationary behavior is described by Gibbs--like measures. For those models the dynamics can be trapped for a very long time in states which are very different from the ones typical of stationarity. This phenomenon can be recasted in the framework of metastability theory which is typical of Statistical Mechanics. In this paper we consider a model presenting two not degenerate in energy metastable states which form a series, in the sense that, when the dynamics is started at one of them, before reaching stationarity, the system must necessarily visit the second one. We discuss a rule for combining the exit times from each of the metastable states

Services within a busy period of an M/M/1 queue and Dyck paths

We analyze the service times of customers in a stable M/M/1 queue in equilibrium depending on their position in a busy period. We give the law of the service of a customer at the beginning, at the end, or in the middle of the busy period. It enables as a by-product to prove that the process of instants of beginning of services is not Poisson. We then proceed to a more precise analysis. We consider a family of polynomial generating series associated with Dyck paths of length 2n and we show that they provide the correlation function of the successive services in a busy period with (n+1) customers

Nonsequential Double Ionization with Polarization-gated Pulses

We investigate laser-induced nonsequential double ionization by a polarization-gated laser pulse, constructed employing two counter-rotating circularly polarized few cycle pulses with a time delay $T_{d}$. We address the problem within a classical framework, and mimic the behavior of the quantum-mechanical electronic wave packet by means of an ensemble of classical electron trajectories. These trajectories are initially weighted with the quasi-static tunneling rate, and with suitably chosen distributions for the momentum components parallel and perpendicular to the laser-field polarization, in the temporal region for which it is nearly linearly polarized. We show that, if the time delay $T_{d}$ is of the order of the pulse length, the electron-momentum distributions, as functions of the parallel momentum components, are highly asymmetric and dependent on the carrier-envelope (CE) phase. As this delay is decreased, this asymmetry gradually vanishes. We explain this behavior in terms of the available phase space, the quasi-static tunneling rate and the recollision rate for the first electron, for different sets of trajectories. Our results show that polarization-gating technique may provide an efficient way to study the NSDI dynamics in the single-cycle limit, without employing few-cycle pulses.Comment: 17 pages, 6 figure

Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109

The Impact of Pre-existing Comorbidities and Therapeutic Interventions on COVID-19.

Evidence from the global outbreak of SARS-CoV-2 has clearly demonstrated that individuals with pre-existing comorbidities are at a much greater risk of dying from COVID-19. This is of great concern for individuals living with these conditions, and a major challenge for global healthcare systems and biomedical research. Not all comorbidities confer the same risk, however, many affect the function of the immune system, which in turn directly impacts the response to COVID-19. Furthermore, the myriad of drugs prescribed for these comorbidities can also influence the progression of COVID-19 and limit additional treatment options available for COVID-19. Here, we review immune dysfunction in response to SARS-CoV-2 infection and the impact of pre-existing comorbidities on the development of COVID-19. We explore how underlying disease etiologies and common therapies used to treat these conditions exacerbate COVID-19 progression. Moreover, we discuss the long-term challenges associated with the use of both novel and repurposed therapies for the treatment of COVID-19 in patients with pre-existing comorbidities