826 research outputs found
CDH1 Testing: Can it Predict the Prophylactic or Therapeutic Nature of Total Gastrectomy in Hereditary Diffuse Gastric Cancer?
More Controversy than Ever – Challenges and Promises Towards Personalized Treatment of Gastric Cancer
SEASONAL VARIATION OF CHEMICAL COMPOSITION AND DRY MATTER DIGESTIBILITY OF RANGELANDS IN NW GREECE
This study was carried out to determine the chemical composition and in vitro dry matter digestibility of grazable material, during the growing season of plants, in three different altitudinal zones, in native rangelands, northwestern Greece. Samples were collected during the period from May to October of the years 2004 and 2005. Sample collection was accomplished by cutting aboveground biomass at a height similar to that grazed by small ruminants. The results
showed that herbage production was significantly affected (P<0.001) by sampling year, growing season and altitudinal zone respectively, as well as (P<0.01) by the “month x year” and (P<0.05) “altitude x month” interactions. CP, ash, EE and CF content and IVDMD affected significantly (P<0.01) by both harvest month and altitudinal zone, while there was no significant affection by the sampling year and the interaction between altitude, month and year (except EE which affected (P<0.01) by the “month x year” interaction). Herbage production strongly related (P<0.01) to the altitude (r= +0.247), harvest month (r= -0.479) and CP content (r= -0.274). IVDMD related positively (P<0.01) to CP (r= +0.729), ash (r= +0.369) and EE (r= +0.351) content and negatively to harvest month (r= -0.779) and to CF content (r= -0.663). It was recommended that additional protein sources should be supplied in order to cover the needs
of the grazing animals. It is necessary the transhumance of herds from lower to higher altitude for better utilization of rangelands
Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsing
The sliding window approach provides an elegant way to handle contexts of
sizes larger than the Transformer's input window, for tasks like language
modeling. Here we extend this approach to the sequence-to-sequence task of
document parsing. For this, we exploit recent progress in transition-based
parsing to implement a parser with synchronous sliding windows over source and
target. We develop an oracle and a parser for document-level AMR by expanding
on Structured-BART such that it leverages source-target alignments and
constrains decoding to guarantee synchronicity and consistency across
overlapping windows. We evaluate our oracle and parser using the Abstract
Meaning Representation (AMR) parsing 3.0 corpus. On the Multi-Sentence
development set of AMR 3.0, we show that our transition oracle loses only 8\%
of the gold cross-sentential links despite using a sliding window. In practice,
this approach also results in a high-quality document-level parser with
manageable memory requirements. Our proposed system performs on par with the
state-of-the-art pipeline approach for document-level AMR parsing task on
Multi-Sentence AMR 3.0 corpus while maintaining sentence-level parsing
performance
AMR Parsing with Instruction Fine-tuned Pre-trained Language Models
Instruction fine-tuned language models on a collection of instruction
annotated datasets (FLAN) have shown highly effective to improve model
performance and generalization to unseen tasks. However, a majority of standard
parsing tasks including abstract meaning representation (AMR), universal
dependency (UD), semantic role labeling (SRL) has been excluded from the FLAN
collections for both model training and evaluations. In this paper, we take one
of such instruction fine-tuned pre-trained language models, i.e. FLAN-T5, and
fine-tune them for AMR parsing. Our extensive experiments on various AMR
parsing tasks including AMR2.0, AMR3.0 and BioAMR indicate that FLAN-T5
fine-tuned models out-perform previous state-of-the-art models across all
tasks. In addition, full fine-tuning followed by the parameter efficient
fine-tuning, LoRA, further improves the model performances, setting new
state-of-the-arts in Smatch on AMR2.0 (86.4), AMR3.0 (84.9) and BioAMR (82.3)
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