Search results for "Trie"
showing 10 items of 4468 documents
Physiological advantages of dwarfing in surviving extinctions in high-CO2 oceans
2015
Excessive CO 2 in the present-day ocean-atmosphere system is causing ocean acidification, and is likely to cause a severe biodiversity decline in the future, mirroring effects in many past mass extinctions. Fossil records demonstrate that organisms surviving such events were often smaller than those before, a phenomenon called the Lilliput effect. Here, we show that two gastropod species adapted to acidified seawater at shallow-water CO 2 seeps were smaller than those found in normal pH conditions and had higher mass-specific energy consumption but significantly lower whole-animal metabolic energy demand. These physiological changes allowed the animals to maintain calcification and to parti…
Airborne Measurements of Contrail Ice Properties—Dependence on Temperature and Humidity
2021
The largest share in the climate impact of aviation results from cirrus clouds. Here, the dependence of microphysical contrail ice properties and extinction on temperature and humidity is investigated. Contrail measurements were performed at various altitudes during the 2018 ECLIF II/NDMAX campaign with the NASA DC-8 chasing the DLR A320. Ice number concentrations and contrail extinction coefficients are largest at altitudes near 9.5 km, typical for short- and medium-range air traffic. At higher altitudes near 11.5 km, low ambient water vapor concentrations lead to smaller contrail particle sizes and lower extinction coefficients. In addition, contrails were detected below 8.2 km near the S…
F. Dostojevskis un N. Gogolis (garstāsti «Saimniece» un «Briesmīgā atriebība»).
2022
Darbs veltīts fantastisku motīvu meklēšanai, kas apvieno F. Dostojevska stāstu “saimniece” un N. Gogoļa “briesmīgā atriebība”. Darba mērķis ir izsekot, kā F. Dostojevska darbos tiek īstenota rakstnieku radošā pēctece: “Saimniece” un “Briesmīgā atriebība” N.Gogoļa. Atklāt mistiskos motīvus. Autors veic abu redakciju pretstatīšanas analīzi, izmantojot informācijas apkopošanu. Darbs tiek sadalīts divās daļās. Pirmajā tiek izklāstīti galvenie teorētiskie jautājumi, kuri nepieciešami pētījuma veikšanai, otrajā daļā, savukārt, saistībā ar uzstādītajiem uzdevumiem. Šis pētījums rada interesi to filologu literatūras zinātnieku vidū, kuri ir ieinteresēti rakstniekam daiļrades, kā arī fantastisku mot…
Higgs boson studies at the Tevatron
2013
We combine searches by the CDF and D0 Collaborations for the standard model Higgs boson with mass in the range 90-200 GeV/c2 produced in the gluon-gluon fusion, WH, ZH, tt̄H, and vector boson fusion processes, and decaying in the H→bb̄, H→W+W-, H→ZZ, H→τ+τ-, and H→γγ modes. The data correspond to integrated luminosities of up to 10 fb-1 and were collected at the Fermilab Tevatron in pp̄ collisions at √s=1.96 TeV. The searches are also interpreted in the context of fermiophobic and fourth generation models. We observe a significant excess of events in the mass range between 115 and 140 GeV/c2. The local significance corresponds to 3.0 standard deviations at mH=125 GeV/c2, consistent with the…
Evidence for a Particle Produced in Association with Weak Bosons and Decaying to a Bottom-Antibottom Quark Pair in Higgs Boson Searches at the Tevatr…
2012
Aaltonen, T. et al.
A Neural Turing~Machine for Conditional Transition Graph Modeling
2019
Graphs are an essential part of many machine learning problems such as analysis of parse trees, social networks, knowledge graphs, transportation systems, and molecular structures. Applying machine learning in these areas typically involves learning the graph structure and the relationship between the nodes of the graph. However, learning the graph structure is often complex, particularly when the graph is cyclic, and the transitions from one node to another are conditioned such as graphs used to represent a finite state machine. To solve this problem, we propose to extend the memory based Neural Turing Machine (NTM) with two novel additions. We allow for transitions between nodes to be inf…
Uncommon Suffix Tries
2011
Common assumptions on the source producing the words inserted in a suffix trie with $n$ leaves lead to a $\log n$ height and saturation level. We provide an example of a suffix trie whose height increases faster than a power of $n$ and another one whose saturation level is negligible with respect to $\log n$. Both are built from VLMC (Variable Length Markov Chain) probabilistic sources; they are easily extended to families of sources having the same properties. The first example corresponds to a ''logarithmic infinite comb'' and enjoys a non uniform polynomial mixing. The second one corresponds to a ''factorial infinite comb'' for which mixing is uniform and exponential.
ASR performance prediction on unseen broadcast programs using convolutional neural networks
2018
In this paper, we address a relatively new task: prediction of ASR performance on unseen broadcast programs. We first propose an heterogenous French corpus dedicated to this task. Two prediction approaches are compared: a state-of-the-art performance prediction based on regression (engineered features) and a new strategy based on convolutional neural networks (learnt features). We particularly focus on the combination of both textual (ASR transcription) and signal inputs. While the joint use of textual and signal features did not work for the regression baseline, the combination of inputs for CNNs leads to the best WER prediction performance. We also show that our CNN prediction remarkably …
Multilingual Clustering of Streaming News
2018
Clustering news across languages enables efficient media monitoring by aggregating articles from multilingual sources into coherent stories. Doing so in an online setting allows scalable processing of massive news streams. To this end, we describe a novel method for clustering an incoming stream of multilingual documents into monolingual and crosslingual story clusters. Unlike typical clustering approaches that consider a small and known number of labels, we tackle the problem of discovering an ever growing number of cluster labels in an online fashion, using real news datasets in multiple languages. Our method is simple to implement, computationally efficient and produces state-of-the-art …
Investigating label suggestions for opinion mining in German Covid-19 social media
2021
This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. We develop guidelines to conduct a controlled annotation study with social science students and find that suggestions from a model trained on a small, expert-annotated dataset already lead to a substantial improvement - in terms of inter-annotator agreement(+.14 Fleiss' $\kappa$) and annotation quality - compared to students that do not receive any label suggestions. We further find that label suggestions from interactively trained models do not lead to an improvement over suggestions from a stat…