11,207 research outputs found
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
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