Conformative Filtering for Implicit Feedback Data

Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and is domain independent. However, there is a lack of negative examples. Previous work tackles this problem by assuming that users are not interested or not as much interested in the unconsumed items. Those assumptions are often severely violated since non-consumption can be due to factors like unawareness or lack of resources. Therefore, non-consumption by a user does not always mean disinterest or irrelevance. In this paper, we propose a novel method called Conformative Filtering (CoF) to address the issue. The motivating observation is that if there is a large group of users who share the same taste and none of them have consumed an item before, then it is likely that the item is not of interest to the group. We perform multidimensional clustering on implicit feedback data using hierarchical latent tree analysis (HLTA) to identify user `tastes' groups and make recommendations for a user based on her memberships in the groups and on the past behavior of the groups. Experiments on two real-world datasets from different domains show that CoF has superior performance compared to several common baselines

Connectivity and Performance Tradeoffs in the Cascade Correlation Learning Architecture

The Cascade Correlation [1] is a very flexible, efficient and fast algorithm for supervised learning. It incrementally builds the network by adding hidden units one at a time, until the desired input/output mapping is achieved. It connects all the previously installed units to the new unit being added. Consequently, each new unit in effect adds a new layer and the fan–in of the hidden and output units keeps on increasing as more units get added. The resulting structure could be hard to implement in VLSI, because the connections are irregular and the fan-in is unbounded. Moreover, the depth or the propagation delay through the resulting network is directly proportional to the number of units and can be excessive. We have modified the algorithm to generate networks with restricted fan-in and small depth (propagation delay) by controlling the connectivity. Our results reveal that there is a tradeoff between connectivity and other performance attributes like depth, total number of independent parameters, learning time, etc. When the number of inputs or outputs is small relative to the size of the training set, a higher connectivity usually leads to faster learning, and fewer independent parameters, but it also results in unbounded fan-in and depth. Strictly layered architectures with restricted connectivity, on the other hand, need more epochs to learn and use more parameters, but generate more regular structures, with smaller, limited fan-in and significantly smaller depth (propagation delay), and may be better suited for VLSI implementations. When the number of inputs or outputs is not very small compared to the size of the training set, however, a strictly layered topology is seen to yield an overall better performance

Switching cloud cover and dynamical regimes from open to closed Benard cells in response to the suppression of precipitation by aerosols

International audienceThe dynamic structure of the atmospheric marine boundary layer (MBL) supports two distinct states of cloud cover: closed and open Benard cellular convection. Closed cells are nearly fully cloud covered, while the open cells have <40% cloud cover. Here we show that aerosols have a greater than expected impact on the cloud cover by changing the mode of cellular convection. By suppressing precipitation aerosols can reverse the direction of the airflow, converting the cloud structure from open to closed cells and doubling the cloud cover. The two states possess positive feedbacks for self maintenance, so that small changes of the conditions can lead to bifurcation of the MBL cloud regime. The transition occurs at near pristine background level of aerosols, creating a large sensitivity of cloud radiative forcing to very small changes in aerosols at the MBL. This can have a major impact on global temperatures

Photoinduced melting of superconductivity in the high-Tc superconductor La2-xSrxCuO4 probed by time-resolved optical and THz techniques

Dynamics of depletion and recovery of superconducting state in La2-xSrxCuO_4 thin films is investigated utilizing optical pump-probe and optical pump - THz probe techniques as a function of temperature and excitation fluence. The absorbed energy density required to suppress superconductivity is found to be about 8 times higher than the thermodynamically determined condensation energy density and nearly temperature independent between 4 and 25 K. These findings indicate that during the time when superconducting state suppression takes place (~0.7 ps), a large part (nearly 90%) of the energy is transferred to the phonons with energy lower than twice the maximum value of of the SC gap and only 10% is spent on Cooper pair breaking.Comment: 8 pages, 5 figure

Discordance between physical symptoms versus perception of severity by women with nausea and vomiting in pregnancy (NVP)

BACKGROUND: Nausea and vomiting in pregnancy (NVP) is a multifaceted condition that affects more than half of pregnant women and can range in severity from mild nausea to severe dehydration. Presently physicians evaluate mostly physical symptoms of NVP in trying to assess the severity of the condition. The objective of this study was to investigate how factors, other than the physical morbidity of nausea and vomiting, influence self-perception of NVP by affected women. METHODS: Five hundred women with NVP calling a 1–800 NVP Healthline were asked to rate their NVP severity and report their nausea duration and number of vomiting/retching episodes. RESULTS: Nausea and vomiting/retching correlated significantly but very poorly with self-assessment of NVP severity. There was also a correlation between nausea duration and vomiting/retching frequency however the correlations were weak and overall physical symptoms could only explain 14% of the variability of women's feelings and perceptions through multivariate analysis. CONCLUSIONS: Physical symptoms weakly correlate with self-assessment of NVP severity. Other aspects of this condition, most probably psychosocial, influence women's perception of NVP severity

Thermodynamic Properties of Generalized Exclusion Statistics

We analytically calculate some thermodynamic quantities of an ideal $g$-on gas obeying generalized exclusion statistics. We show that the specific heat of a $g$-on gas ($g \neq 0$) vanishes linearly in any dimension as $T \to 0$ when the particle number is conserved and exhibits an interesting dual symmetry that relates the particle-statistics at $g$ to the hole-statistics at $1/g$ at low temperatures. We derive the complete solution for the cluster coefficients $b_l(g)$ as a function of Haldane's statistical interaction $g$ in $D$ dimensions. We also find that the cluster coefficients $b_l(g)$ and the virial coefficients $a_l(g)$ are exactly mirror symmetric ($l$=odd) or antisymmetric ($l$=even) about $g=1/2$. In two dimensions, we completely determine the closed forms about the cluster and the virial coefficients of the generalized exclusion statistics, which exactly agree with the virial coefficients of an anyon gas of linear energies. We show that the $g$-on gas with zero chemical potential shows thermodynamic properties similar to the photon statistics. We discuss some physical implications of our results.Comment: 24 pages, Revtex, Corrected typo

Post- and peritraumatic stress in disaster survivors: An explorative study about the influence of individual and event characteristics across different types of disasters

Background: Examination of existing research on posttraumatic adjustment after disasters suggests that survivors’ posttraumatic stress levels might be better understood by investigating the influence of the characteristics of the event experienced on how people thought and felt, during the event as well as afterwards. Objective: To compare survivors’ perceived post- and peritraumatic emotional and cognitive reactions across different types of disasters. Additionally, to investigate individual and event characteristics. Design: In a European multi-centre study, 102 survivors of different disasters terror attack, flood, fire and collapse of a building were interviewed about their responses during the event. Survivors’ perceived posttraumatic stress levels were assessed with the Impact of Event Scale-Revised (IES-R). Peritraumatic emotional stress and risk perception were rated retrospectively. Influences of individual characteristics, such as socio-demographic data, and event characteristics, such as time and exposure factors, on post- and peritraumatic outcomes were analyzed. Results: Levels of reported post- and peritraumatic outcomes differed significantly between types of disasters. Type of disaster was a significant predictor of all three outcome variables but the factors gender, education, time since event, injuries and fatalities were only significant for certain outcomes. Conclusion: Results support the hypothesis that there are differences in perceived post- and peritraumatic emotional and cognitive reactions after experiencing different types of disasters. However, it should be noted that these findings were not only explained by the type of disaster itself but also by individual and event characteristics. As the study followed an explorative approach, further research paths are discussed to better understand the relationships between variables

Equations discovery of organized cloud fields: Stochastic generator and dynamical insights

The emergence of organized multiscale patterns resulting from convection is ubiquitous, observed throughout different cloud types. The reproduction of such patterns by general circulation models remains a challenge due to the complex nature of clouds, characterized by processes interacting over a wide range of spatio-temporal scales. The new advances in data-driven modeling techniques have raised a lot of promises to discover dynamical equations from partial observations of complex systems. This study presents such a discovery from high-resolution satellite datasets of continental cloud fields. The model is made of stochastic differential equations able to simulate with high fidelity the spatio-temporal coherence and variability of the cloud patterns such as the characteristic lifetime of individual clouds or global organizational features governed by convective inertia gravity waves. This feat is achieved through the model's lagged effects associated with convection recirculation times, and hidden variables parameterizing the unobserved processes and variables.Comment: 11 pages, 9 figure