94 research outputs found
Detecting (Un)Important Content for Single-Document News Summarization
We present a robust approach for detecting intrinsic sentence importance in
news, by training on two corpora of document-summary pairs. When used for
single-document summarization, our approach, combined with the "beginning of
document" heuristic, outperforms a state-of-the-art summarizer and the
beginning-of-article baseline in both automatic and manual evaluations. These
results represent an important advance because in the absence of cross-document
repetition, single document summarizers for news have not been able to
consistently outperform the strong beginning-of-article baseline.Comment: Accepted By EACL 201
Effects of chlorinated dioxins and furans on avian species : insights from in Ovo studies
Many physiological responses to dioxin-like compounds (DLCs), including polychlorinated dibenzodioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) are mediated by the aryl-hydrocarbon receptor (AhR). In birds, activation of the AhR stimulates the transcription of cytochrome P4501A (CYP1A) genes, including CYP1A4 and CYP1A5, and ultimately leads to expression of biotransformation enzymes, including ethoxyresorufin-O-deethylase (EROD). It is well established that potencies of different DLCs range over several orders of magnitude. There is also a wide variation among birds in their responsiveness to DLCs both in efficacy and threshold for effects. A molecular basis for this differential sensitivity has been suggested. Specifically, a comparison of the AhR ligand-binding domain (LBD) indicated that key amino acid residues are predictive of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) sensitivity. Based on sequencing of the AhR LBD from numerous avian species a sensitive classification scheme has been proposed (in order of decreasing sensitivity, chicken (type I; sensitive) > Common pheasant (type II; moderately sensitive) > Japanese quail (type III; insensitive)). A series of egg injection studies with White-leghorn chicken (Gallus gallus domesticus), Common pheasant (Phasianus colchicus) and Japanese quail (Coturnix japonica) were performed to determine whether molecular and biochemical markers of exposure to DLCs are predictive of the proposed classification scheme. In addition, I was interested in determining whether this classification scheme applies to other DLCs, specifically dibenzofurans. Determining which species are "chicken- like", "pheasant-like" and "quail-like" in their responses to DLCs should allow more refined risk assessments to be conducted as there would be less uncertainty about the potential effects of DLCs in those species for which population-level studies do not exist.
Several concentrations of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), 2,3,4,7,8-pentachlorodibenzofuran (PeCDF), or 2,3,7,8-tetrachlorodibenzofuran (TCDF) (triolein vehicle) were injected into the air cells of Japanese quail, Common pheasant and chicken eggs. Liver from 14 d post-hatch chicks was harvested for analysis of CYP1A4 and CYP1A5 mRNA abundance by quantitative real-time PCR (Q-PCR), and EROD activity. Lowest observed effective concentration (LOEC) and relative potency (ReP) values for CYP1A mRNA abundance and EROD activity were determined and used to make comparisons of sensitivity between each species and DLC potency within each species.
The TCDD is widely considered to be the most potent DLC and this is supported by the rank order of LOEC values for CYP1A5 mRNA abundance in White-leghorn chicken (TCDD > PeCDF > TCDF). CYP1A4 mRNA abundance and EROD activity in White-leghorn chicken were significantly increased in the lowest dose exposure groups of each of the three DLCs, so the potency of these compounds based on these endpoints was not established. Interestingly, TCDD was not the most potent DLC in Common pheasant and Japanese quail. In Common pheasant, PeCDF is the most potent as a CYP1A4 mRNA inducer, followed by TCDD and TCDF. However, TCDF was the most potent EROD activity inducer for Common pheasant, followed by PeCDF, and then TCDD. No significant increases were found in CYP1A5 mRNA abundance in pheasant within the tested dose ranges for all the three DLCs. No significant increases in either CYP1A5 mRNA abundance or EROD activity were found in Japanese quail. In addition, PeCDF and TCDF, but not TCDD, significantly increased CYP1A4 mRNA abundance.
According to the predicted relative sensitivity by comparing the AhR LBD amino acid sequences, the White-leghorn chicken is more responsive to DLCs than the Common pheasant which is more responsive than the Japanese quail. By comparing the relative sensitivity calculated based on the LOEC values from my study, the sensitivity order to TCDD and TCDF support the proposed molecular based species sensitivity classification scheme (chicken > pheasant > quail), while pheasant is almost as sensitive as chicken to PeCDF ( pheasant ¡Ý chicken > quail).
Taken together, the data suggest that TCDD is the most potent DLC in White-leghorn chicken, but not in Common pheasant, or or Japanese quail. The data suggest that in type II avian species PeCDF may be more potent than TCDD. In addition, I found in my study that different biomarkers have different responses, which depends on species and chemicals as well. These data provide further insight into avian sensitivities to DLCs
Self Supervision Does Not Help Natural Language Supervision at Scale
Self supervision and natural language supervision have emerged as two
exciting ways to train general purpose image encoders which excel at a variety
of downstream tasks. Recent works such as M3AE and SLIP have suggested that
these approaches can be effectively combined, but most notably their results
use small pre-training datasets (<50M samples) and don't effectively reflect
the large-scale regime (>100M examples) that is commonly used for these
approaches. Here we investigate whether a similar approach can be effective
when trained with a much larger amount of data. We find that a combination of
two state of the art approaches: masked auto-encoders, MAE and contrastive
language image pre-training, CLIP provides a benefit over CLIP when trained on
a corpus of 11.3M image-text pairs, but little to no benefit (as evaluated on a
suite of common vision tasks) over CLIP when trained on a large corpus of 1.4B
images. Our work provides some much needed clarity into the effectiveness (or
lack thereof) of self supervision for large-scale image-text training
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