292 research outputs found
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Parents adaptively use anaphora during parent-child social interaction
Anaphora, a ubiquitous feature of natural language, poses a particular challenge to young children as they first learn language due to its referential ambiguity. In spite of this, parents and caregivers use anaphora frequently in child-directed speech, potentially presenting a risk to effective communication if children do not yet have the linguistic capabilities of resolving anaphora successfully. Through an eye-tracking study in a naturalistic free-play context, we examine the strategies that parents employ to calibrate their use of anaphora to their child's linguistic development level. We show that, in this way, parents are able to intuitively scaffold the complexity of their speech such that greater referential ambiguity does not hurt overall communication success
Quantification of methane emissions from cattle farms, using the tracer gas dispersion method
In Denmark, agriculture is the largest source of anthropogenic methane emissions (81%), mainly from cattle (dairy and beef) farms. Whole-farm methane emissions were quantified at nine Danish cattle farms, using the tracer gas dispersion method. Five to six measurement campaigns were carried out at each farm, covering a full year. Of the nine cattle farms, seven were home to dairy cows and two to beef cattle. The farms represented typical breeds, housing and management systems used in Denmark. Whole-farm methane emission rates ranged from 0.7 to 28 kg h−1, with the highest measurements seen at locations with the highest number of animals. Emissions tended to be higher from August to October, due to elevated temperatures and high amounts of stored manure during this period of the year. The average emission factor (EF) for dairy cow farms was 26 \ub1 8.5 g Livestock Unit (LU)−1 h−1, whereas it was 16 \ub1 4.1 LU−1 h−1 for beef cattle farms, i.e. 38% lower for the latter. The use of deep litter house management explained some of the differences found in the EFs for dairy cows. Methane emission rates estimated using IPCC models and national guidelines tended, on average for all farms and measurements, to be underestimated by 35% in comparison with the measured methane emissions, for all models and farms. The results suggest that future improvements to inventory models should focus on enteric methane emissions from beef cattle and manure methane emissions for both dairy cows and beef cattle, especially from deep litter management
Remote sensing quantification of methane emissions from 10 biogas plants in Denmark and Germany
Ammonia and methane emissions from dairy concentrated animal feeding operations in California, using mobile optical remote sensing
Dairy concentrated animal feeding operations (CAFOs) are significant sources of methane (CH4) and ammonia (NH3) emissions in the San Joaquin Valley, California. Optical techniques, namely, remote sensing by Solar Occultation Flux (SOF) and Mobile extractive FTIR (MeFTIR), were used to measure NH3 air column and ground air concentrations of NH3 and CH4, respectively. Campaigns were performed in May and October 2019 and covered 14 dairies located near Bakersfield and Tulare, California. NH3 and CH4 emission rates from single CAFOs averaged 101.9 \ub1 40.6 kgNH3/h and 437.7 \ub1 202.0 kgCH4/h, respectively, corresponding to emission factors (EFs) per livestock unit of 9.1 \ub1 2.7 gNH3/LU/h and 40.1 \ub1 17.8 gCH4/LU/h. The NH3 emissions had a median standard uncertainty of 17% and an expanded uncertainty (95% Confidence Interval (CI)) of 37%; meanwhile, CH4 emissions estimates had greater uncertainty, median of 25% and 53% (in the 95% CI). Decreasing NH3 to CH4 ratios and NH3 EFs from early afternoon (13:00) to early night (19:00) indicated a diurnal emission pattern with lower ammonia emissions during the night. On average, measured NH3 emissions were 28% higher when compared to daytime emission rates reported in the National Emissions Inventory (NEI) and modeled according to diurnal variation. Measured CH4 emissions were 60% higher than the rates reported in the California Air Resources Board (CARB) inventory. However, comparison with airborne measurements showed similar emission rates. This study demonstrates new air measurement methods, which can be used to quantify emissions over large areas with high spatial resolution and in a relatively short time period. These techniques bridge the gap between satellites and individual CAFOs measurements
Fyns Amt. Forurenet lokalitet nr. 485-20 - Tidligere maskinfabrik, Sortebrovej 26, 5690 Tommerup:Undersøgelser til vurdering af stimuleret in-situ reduktiv deklorering og kemisk oxidation ved oprensning af grundvandsforurening
Development and validation of a method for determining VOCs by using muti-sorbent tubes and ATD-GC-MS
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds
In order for artificial agents to successfully perform tasks in changing
environments, they must be able to both detect and adapt to novelty. However,
visual novelty detection research often only evaluates on repurposed datasets
such as CIFAR-10 originally intended for object classification, where images
focus on one distinct, well-centered object. New benchmarks are needed to
represent the challenges of navigating the complex scenes of an open world. Our
new NovelCraft dataset contains multimodal episodic data of the images and
symbolic world-states seen by an agent completing a pogo stick assembly task
within a modified Minecraft environment. In some episodes, we insert novel
objects of varying size within the complex 3D scene that may impact gameplay.
Our visual novelty detection benchmark finds that methods that rank best on
popular area-under-the-curve metrics may be outperformed by simpler
alternatives when controlling false positives matters most. Further multimodal
novelty detection experiments suggest that methods that fuse both visual and
symbolic information can improve time until detection as well as overall
discrimination. Finally, our evaluation of recent generalized category
discovery methods suggests that adapting to new imbalanced categories in
complex scenes remains an exciting open problem.Comment: Published in Transactions on Machine Learning Research (03/2023
Harnessing Higher-Order (Meta-)Logic to Represent and Reason with Complex Ethical Theories
The computer-mechanization of an ambitious explicit ethical theory, Gewirth's
Principle of Generic Consistency, is used to showcase an approach for
representing and reasoning with ethical theories exhibiting complex logical
features like alethic and deontic modalities, indexicals, higher-order
quantification, among others. Harnessing the high expressive power of Church's
type theory as a meta-logic to semantically embed a combination of quantified
non-classical logics, our work pushes existing boundaries in knowledge
representation and reasoning. We demonstrate that intuitive encodings of
complex ethical theories and their automation on the computer are no longer
antipodes.Comment: 14 page
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