110 research outputs found
Copy mechanism and tailored training for character-based data-to-text generation
In the last few years, many different methods have been focusing on using
deep recurrent neural networks for natural language generation. The most widely
used sequence-to-sequence neural methods are word-based: as such, they need a
pre-processing step called delexicalization (conversely, relexicalization) to
deal with uncommon or unknown words. These forms of processing, however, give
rise to models that depend on the vocabulary used and are not completely
neural.
In this work, we present an end-to-end sequence-to-sequence model with
attention mechanism which reads and generates at a character level, no longer
requiring delexicalization, tokenization, nor even lowercasing. Moreover, since
characters constitute the common "building blocks" of every text, it also
allows a more general approach to text generation, enabling the possibility to
exploit transfer learning for training. These skills are obtained thanks to two
major features: (i) the possibility to alternate between the standard
generation mechanism and a copy one, which allows to directly copy input facts
to produce outputs, and (ii) the use of an original training pipeline that
further improves the quality of the generated texts.
We also introduce a new dataset called E2E+, designed to highlight the
copying capabilities of character-based models, that is a modified version of
the well-known E2E dataset used in the E2E Challenge. We tested our model
according to five broadly accepted metrics (including the widely used BLEU),
showing that it yields competitive performance with respect to both
character-based and word-based approaches.Comment: ECML-PKDD 2019 (Camera ready version
Evolution and final fate of solar metallicity stars in the mass range 7-15 Msun. I. The transition from AGB to SAGB stars, Electron Capture and Core Collapse Supernovae progenitors
According to a standard initial mass function, stars in the range 7-12 Msun
constitute ~50% (by number) of the stars more massive than ~7 Msun, but, in
spite of this, their evolutionary properties, and in particular their final
fate, are still scarcely studied. In this paper we present a detailed study of
the evolutionary properties of solar metallicity, non rotating stars in the
range 7-15 Msun, from the pre main sequence phase up to the presupernova stage
or up to an advanced stage of the thermally pulsing phase, depending on the
initial mass. We find that (1) the 7.00 Msun develops a degenerate CO core and
evolves as a classical AGB star in the sense that it does not ignite the C
burning reactions; (2) stars with the initial mass M >= 9.22 Msun end their
life as core collapse supernovae; (3) stars in the range 7.50 <= M/Msun <= 9.20
develop a degenerate ONeMg core and evolve through the thermally pulsing SAGB
phase; 4) stars in the mass range 7.50 <= M/Msun <= 8.00 end their life as
hybrid CO/ONeMg- or ONeMg- WD; (5) stars with the initial mass in the range
8.50 <= M/Msun <= 9.20 may potentially explode as electron capture supernovae.Comment: 79 pages, 40 figures, 13 tables. Accepted for publication on ApJ
Recognition self-awareness for active object recognition on depth images
We propose an active object recognition framework that introduces the recognition self-awareness, which is an intermediate level of reasoning to decide which views to cover during the object exploration. This is built first by learning a multi-view deep 3D object classifier; subsequently, a 3D dense saliency volume is generated by fusing together single-view visualization maps, these latter obtained by computing the gradient map of the class label on different image planes. The saliency volume indicates which object parts the classifier considers more important for deciding a class. Finally, the volume is injected in the observation model of a Partially Observable Markov Decision Process (POMDP). In practice, the robot decides which views to cover, depending on the expected ability of the classifier to discriminate an object class by observing a specific part. For example, the robot will look for the engine to discriminate between a bicycle and a motorbike, since the classifier has found that part as highly discriminative. Experiments are carried out on depth images with both simulated and real data, showing that our framework predicts the object class with higher accuracy and lower energy consumption than a set of alternatives
Explosion mechanism of core-collapse supernovae: role of the Si/O interface
We present a simple criterion to predict the explodability of massive stars
based on the density and entropy profiles before collapse. If a pronounced
density jump is present near the Si/Si-O interface, the star will likely
explode. We develop a quantitative criterion by using 1D
simulations where -driven turbulence is included via time-dependent
mixing-length theory. This criterion correctly identifies the outcome of the
supernova more than of the time. We also find no difference in how this
criterion performs on two different sets of progenitors, evolved using two
different stellar evolution codes: FRANEC and KEPLER. The explodability as a
function of mass of the two sets of progenitors is very different, showing: (i)
that uncertainties in the stellar evolution prescriptions influence the
predictions of supernova explosions; (ii) the most important properties of the
pre-collapse progenitor that influence the explodability are its density and
entropy profiles. We highlight the importance that -driven turbulence
plays in the explosion by comparing our results to previous works.Comment: 20 pages, 12 figures, submitted to Ap
Controlling Hallucinations at Word Level in Data-to-Text Generation
Data-to-Text Generation (DTG) is a subfield of Natural Language Generation
aiming at transcribing structured data in natural language descriptions. The
field has been recently boosted by the use of neural-based generators which
exhibit on one side great syntactic skills without the need of hand-crafted
pipelines; on the other side, the quality of the generated text reflects the
quality of the training data, which in realistic settings only offer
imperfectly aligned structure-text pairs. Consequently, state-of-art neural
models include misleading statements - usually called hallucinations - in their
outputs. The control of this phenomenon is today a major challenge for DTG, and
is the problem addressed in the paper.
Previous work deal with this issue at the instance level: using an alignment
score for each table-reference pair. In contrast, we propose a finer-grained
approach, arguing that hallucinations should rather be treated at the word
level. Specifically, we propose a Multi-Branch Decoder which is able to
leverage word-level labels to learn the relevant parts of each training
instance. These labels are obtained following a simple and efficient scoring
procedure based on co-occurrence analysis and dependency parsing. Extensive
evaluations, via automated metrics and human judgment on the standard WikiBio
benchmark, show the accuracy of our alignment labels and the effectiveness of
the proposed Multi-Branch Decoder. Our model is able to reduce and control
hallucinations, while keeping fluency and coherence in generated texts. Further
experiments on a degraded version of ToTTo show that our model could be
successfully used on very noisy settings.Comment: 20 pages, 6 figures, 5 tables (excluding Appendix). Source code:
https://github.com/KaijuML/dtt-multi-branc
I learn. You learn. We learn? An experiment in collaborative concept mapping.
International audienc
Evaluating the effect of pupil dilation on spectral-domain optical coherence tomography measurements and their quality score
BACKGROUND: Spectral-domain optical coherence tomography (SD-OCT) provides fast scan speed and high scan resolution improving its diagnostic accuracy. The purpose of this study was to evaluate if SD-OCT measurements and their quality score are influenced by pupil dilation. METHODS: Retinal nerve fiber layer thickness (RNFL), ganglion cell complex (GCC) and optic nerve head (ONH) were measured in one eye of 57 glaucoma patients and 36 healthy subjects using spectral domain optical coherence tomography (SD-OCT) before and after pupil dilation. Comparisons were made between measurements and their quality score pre- and post dilation (Signal Strength Index, SSI). Overall RNFL, average GCC and ONH rim volume were considered in the analysis. RESULTS: No statistically significant differences were found between pre- and post-dilation measurements in both groups (glaucoma: RNFL 80 ± 15 μm vs 80 ± 16 μm, p = 0.87; GCC 81.35 ± 13.4 μm vs 81.10 ± 13.14 μm, p = 0.92; ONH 0.05 ± 0.11 mm(3) vs 0.04 ± 0.07 mm(3), p = 0.74; controls RNFL 99 ± 12 μm vs 98 ± 14 μm, p = 0.70; GCC 92.12 ± 6.7 μm vs 91.54 ± 7.05 μm, p = 0.72; ONH 0.11 ± 0.1 mm(3) vs 0.04 ± 0.07 mm(3), p = 0.36) nor between pre- and post-dilation quality score (glaucoma SSI RNFL 54.3 ± 10.3 vs 51.7 ± 18.1, p = 0.12; SSI GCC 58 ± 9.5 vs 57 ± 8.09, p = 0.55; SSI ONH 48.5 ± 7.6 vs 46.6 ± 7.2, p = 0.16; controls SSI RNFL 57 ± 10.3 vs 54 ± 9.31, p = 0.2; SSI GCC 60.9 ± 8.1 vs 58.8 ± 7.3, p = 0.3; SSI ONH 51.5 ± 8.9 vs 50.4 ± 8.3, p = 0.59). CONCLUSION: Pupil dilation doesn’t affect SD-OCT measurements and their quality score
Levels of plasma homocysteine in pseudoexfoliation glaucoma.
BACKGROUND: To examine levels of serum homocysteine (Hcy), vitamin B12 and folic acid in patients with pseudoexfoliation glaucoma (PEXG), primary open-angle glaucoma (POAG), and healthy control subjects.
METHODS: This study included 36 patients with PEXG, 40 with POAG, and 40 age-matched healthy subjects. Fasting plasma Hcy concentrations and levels of serum vitamin B12 and folic acid were measured using competitive chemiluminescent enzyme immunoassay; values exceeding 14 μm/l were considered elevated. RESULTS:
Mean plasma Hcy was significantly higher in PEXG (16.55 ± 7.23 μm/l) compared with POAG (13.91 ± 3.61 μm/l) and controls (13.12 ± 5.13 μm/l) (p = 0.03 and p = 0.0007 respectively). There were no statistical differences in serum vitamin B12 and folic acid levels among PEXG, POAG and control subjects (p > 0.05). A moderate, although statistically significant, relationship between Hcy and folic acid levels was found in the PEXG group (R(2) = 0.23, p = 0.003). Hcy levels were found not to be related with folic acid or vitamin B12 in either POAG or control subjects.
CONCLUSIONS: In this study, plasma Hcy is significantly higher in PEXG group than the POAG and control groups. Hyper-Hcy might play a role in the pathogenesis of PEXG. Hyper-Hcy may be an independent factor stressing vasculopathy in addition to pseudoexfoliation, so might be a modifiable risk factor for PEXG
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