Uncertainty in Crowd Data Sourcing under Structural Constraints

Applications extracting data from crowdsourcing platforms must deal with the uncertainty of crowd answers in two different ways: first, by deriving estimates of the correct value from the answers; second, by choosing crowd questions whose answers are expected to minimize this uncertainty relative to the overall data collection goal. Such problems are already challenging when we assume that questions are unrelated and answers are independent, but they are even more complicated when we assume that the unknown values follow hard structural constraints (such as monotonicity). In this vision paper, we examine how to formally address this issue with an approach inspired by [Amsterdamer et al., 2013]. We describe a generalized setting where we model constraints as linear inequalities, and use them to guide the choice of crowd questions and the processing of answers. We present the main challenges arising in this setting, and propose directions to solve them.Comment: 8 pages, vision paper. To appear at UnCrowd 201

Climate strategy with CO2 capture from the air

Ce texte porte sur la question de la réversibilité à long terme des émissions de CO2. Il donne un exemple de technologies de capture directe à partir de l'air, qui permet de borner le coût marginal de réduction du CO2 dans toute l'économie. Il exploré les aspects thermodynamiques de la capture du dioxyde de carbone à partir de l'air. Le modèle DIAM a été étendu pour prendre en compte ces options et examiner les conséquences sur les politiques climatiques optimales.Capture du carbone, changement climatique, modélisation intégrée, politique climatique

Influence of the "second gap" on the transparency-conductivity compromise in transparent conducting oxides: an ab initio study

Transparent conducting oxides (TCOs) are essential to many technologies. These materials are doped (\emph{n}- or \emph{p}-type) oxides with a large enough band gap (ideally $>$3~eV) to ensure transparency. However, the high carrier concentration present in TCOs lead additionally to the possibility for optical transitions from the occupied conduction bands to higher states for \emph{n}-type materials and from lower states to the unoccupied valence bands for \emph{p}-type TCOs. The "second gap" formed by these transitions might limit transparency and a large second gap has been sometimes proposed as a design criteria for high performance TCOs. Here, we study the influence of this second gap on optical absorption using \emph{ab initio} computations for several well-known \emph{n}- and \emph{p}-type TCOs. Our work demonstrates that most known \emph{n}-type TCOs do not suffer from second gap absorption in the visible even at very high carrier concentrations. On the contrary, \emph{p}-type oxides show lowering of their optical transmission for high carrier concentrations due to second gap effects. We link this dissimilarity to the different chemistries involved in \emph{n}- versus typical \emph{p}-type TCOs. Quantitatively, we show that second gap effects lead to only moderate loss of transmission (even in p-type TCOs) and suggest that a wide second gap, while beneficial, should not be considered as a needed criteria for a working TCO.Comment: 6 pages, 4 figures, APS March Meetin

Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks

Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models can provide useful user representations for recommendation. However, current RNN modeling approaches summarize the user state by only taking into account the sequence of items that the user has interacted with in the past, without taking into account other essential types of context information such as the associated types of user-item interactions, the time gaps between events and the time of day for each interaction. To address this, we propose a new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and more explicitly, in the model dynamics, by parametrizing the hidden unit transitions as a function of context information. We compare our CRNNs approach with RNNs and non-sequential baselines and show good improvements on the next event prediction task

Biochemical Properties of a Decoy Oligodeoxynucleotide Inhibitor of STAT3 Transcription Factor.

Cyclic STAT3 decoy (CS3D) is a second-generation, double-stranded oligodeoxynucleotide (ODN) that mimics a genomic response element for signal transducer and activator of transcription 3 (STAT3), an oncogenic transcription factor. CS3D competitively inhibits STAT3 binding to target gene promoters, resulting in decreased expression of proteins that promote cellular proliferation and survival. Previous studies have demonstrated antitumor activity of CS3D in preclinical models of solid tumors. However, prior to entering human clinical trials, the efficiency of generating the CS3D molecule and its stability in biological fluids should be determined. CS3D is synthesized as a single-stranded ODN and must have its free ends ligated to generate the final cyclic form. In this study, we report a ligation efficiency of nearly 95 percent. The ligated CS3D demonstrated a half-life of 7.9 h in human serum, indicating adequate stability for intravenous delivery. These results provide requisite biochemical characterization of CS3D that will inform upcoming clinical trials