Performance of neural models for named entity recognition degrades over time, becoming stale. This degradation is due to temporal drift, the change in our target variables' statistical properties over time. This issue is especially problematic for so
cial media data, where topics change rapidly. In order to mitigate the problem, data annotation and retraining of models is common. Despite its usefulness, this process is expensive and time-consuming, which motivates new research on efficient model updating. In this paper, we propose an intuitive approach to measure the potential trendiness of tweets and use this metric to select the most informative instances to use for training. We conduct experiments on three state-of-the-art models on the Temporal Twitter Dataset. Our approach shows larger increases in prediction accuracy with less training data than the alternatives, making it an attractive, practical solution.
When intelligent agents communicate to accomplish shared goals, how do these goals shape the agents' language? We study the dynamics of learning in latent language policies (LLPs), in which instructor agents generate natural-language subgoal descript
ions and executor agents map these descriptions to low-level actions. LLPs can solve challenging long-horizon reinforcement learning problems and provide a rich model for studying task-oriented language use. But previous work has found that LLP training is prone to semantic drift (use of messages in ways inconsistent with their original natural language meanings). Here, we demonstrate theoretically and empirically that multitask training is an effective counter to this problem: we prove that multitask training eliminates semantic drift in a well-studied family of signaling games, and show that multitask training of neural LLPs in a complex strategy game reduces drift and while improving sample efficiency.
The success of large-scale contextual language models has attracted great interest in probing what is encoded in their representations. In this work, we consider a new question: to what extent contextual representations of concrete nouns are aligned
with corresponding visual representations? We design a probing model that evaluates how effective are text-only representations in distinguishing between matching and non-matching visual representations. Our findings show that language representations alone provide a strong signal for retrieving image patches from the correct object categories. Moreover, they are effective in retrieving specific instances of image patches; textual context plays an important role in this process. Visually grounded language models slightly outperform text-only language models in instance retrieval, but greatly under-perform humans. We hope our analyses inspire future research in understanding and improving the visual capabilities of language models.
The soil water erosion risk is one of the most important problems and
challenges facing the agricultural process in the Syrian coast today. The aim
of this study was to determine the spatial distribution of dangerous areas of
water erosion in the
basin of the Mrqyieh River using CORINE model. To
achieve this objective, the first phase of the study was to evaluate the soil
erosion viability based on soil characteristics (soil texture, soil depth and
percentage of stones); these properties were classified according to their
influence degree on soil erosion. The potential risk erosion map was based
on crossing all information obtained from soil erodibility, erosivity index
and the degree of slope at study area by using GIS technologies. The land
cover map of the study was produced and classified to two classes
depending on soil protection degree. Then, an actual risk map of soil erosion
was prepared after crossing land cover and potential risk erosion classes of
study sites. This study showed that 14.8% of the studied area facing high
risk of soil erosion, while the soil risk was moderate in 40.4% and low in
44.8% of the study area. The highly risked erosion area was located in the
center, northern and northwest parts of the study area. Moreover, the study
confirmed that the land cover is the most influential factor on soil water
erosion. The results showed that the Corine model for soil water erosion
mapping is a highly effective and cost-effective approach
The aim of this research is to predict the quantities of soil lost by the water erosion in the
Al-Hawiz Dam basin area using GIS and RUSL. R factor was calculated through
matimatical equation after collecting rain data during 2008-2017 from weather
station at
Basel-Al-Assad airport .k value of each soil sampl was calculated after determination of
txture,structure,saturated hydrolic conductivity, and organic matter).a map were prepared
showed local distribution of k values .slop factor was determined as well as using DEM for
studied region, and slop map was introduced in mathematical equation through a GIS to
obtain LS map .NDV used for studied region to calculate C map.To obtain predictive map
of soil lost quantitis ,maps of LS,C,K was multiplicated with R value.
The results showed that R value in studied region 342.78 ,while k factor value was
0.7-0.28.soil with low value concentrated at medium part of studied region,whil slop
factor value was between 0 and 38.87.C factor value was 0.29 at west part and 0.98 at east
part .prediction map of lost quantites was classified in to 4 degrees according erosion risk (
very low risk ,low,medium,high .The results of soil lost quantities were classified in to 4
classes in studied region : very low( 0-5) t/h/year,low( 5-12 ) t/h/year and medium ( 12-24
t/h/year and severe in which soil loss exceeded 24 t/h/year
Water erosion is the most serious environmental problem which cause soil
degradation in watershed areas in Syria cost .for this reason, this study aimed to defined
spatial distribution of water erosion risk for land Bhmra Dm basin using corine mode
l.
Corine model depend on calculating all factor that affect water erosion ,soil erosion
vability ,rain erosivity ,slop and land cover.
The research was performed during the three years from 2011 in the coastal area
“Lattakia and Tartous” The study of soil erosion has been traced in eight occasional sites in
the coastal area, Gradient in its slope degree from 10% to 45% . the eight
sites has also
been studied under the three systems “Forests, burned forests , planted soil”
The results shows, that the dangerous of the Water erosion in the coastal area soils
especially in the slopes that is more than 15%, the drift reached scary figures that ranged
between 32.5 ton/Hectares when the slope was 10%, and 165 ton/hectares when the slope
was 45% in the agricultural system (Where the surface of the soil is semi-disgrace), These
amounts ranged between 9 and 56.5 t / hectares/year in the burned forest system and
between 1.4 and 15 t / hectares/year in the forest system.
The runoff of rain water may range between 24 and 59.20 in the forest system versus
6.8 and 32.8 in the burned forest systems and, finally, between 2.9 and 16.8 in the forest
system.
Soil water erosion is one of the most important factors of soil
degradation. Soil erosion is a process that causes loss of big
amounts of nutrients and organic matter from the topsoil layer and
pollutes the surface water bodies. The USDA- WEPP (Wa
ter
Erosion Prediction Project erosion model) represents a new
generation technology for estimating soil loss by water erosion and
sediment delivery from hillslopes and small watersheds. The main
purpose of this study was evaluating the capability of WEPP model.
This research was conducted in order to determine the impact
of raindrops in terms of force of impact and its relation to rain
intensity as well as the relay rain on the amount of soil eroded
and water drifting due to water erosion .
The study aimedtoalertthe danger oferosionare three types ofsoilsexposedby
calculatingthe amount ofsoilerodedfromthe impact ofthe
Cascadefiverainstormsequalintensity, thosesoils, has been securedsoilsnecessaryfor the
studyofseveral areas ofdiffere
nt provincesin Syria, where he wasplaced in thebasin,
thenoffered forrainstormsbymobile(Rainfall Simulation),andaftereveryrainstormwas
recordedreadingsfor the loss ofsoilfromeachbasinrunoffandinfiltration.
The results of thisstudy showedthe effectin terms oftexturesin thedrift,
themoremechanicalgroupsdriftingin the threesoilsareprimarilysiltgroup, followed by the
mudpack, thensand, and clay soilswith ahigh percentageofsiltmosterosionofclay soilswith
alowpercentageofsilt, followed bysandyclayloamsoils, wherethe amount ofsoillostfromthe
clay soilreachedwith a high contentofsilt147.7t/ h/y, andthe lowclaycontentofsilt118.5t/
h/y, while the soilwithtexturesLummisandyclay, reaching 90.5t / h/y.And soil erosion that
attaches primarily by college carbonates then dispersion ratio of organic matter and then
finally percentage silt.
