3,442 research outputs found
Data Augmentation in Training CNNs: Injecting Noise to Images
Noise injection is a fundamental tool for data augmentation, and yet there is
no widely accepted procedure to incorporate it with learning frameworks. This
study analyzes the effects of adding or applying different noise models of
varying magnitudes to Convolutional Neural Network (CNN) architectures. Noise
models that are distributed with different density functions are given common
magnitude levels via Structural Similarity (SSIM) metric in order to create an
appropriate ground for comparison. The basic results are conforming with the
most of the common notions in machine learning, and also introduce some novel
heuristics and recommendations on noise injection. The new approaches will
provide better understanding on optimal learning procedures for image
classification.Comment: 12 pages, 9 figures, 2 tables, old paper just submitted to arXi
Language-Agnostic Bias Detection in Language Models with Bias Probing
Pretrained language models (PLMs) are key components in NLP, but they contain
strong social biases. Quantifying these biases is challenging because current
methods focusing on fill-the-mask objectives are sensitive to slight changes in
input. To address this, we propose a bias probing technique called LABDet, for
evaluating social bias in PLMs with a robust and language-agnostic method. For
nationality as a case study, we show that LABDet `surfaces' nationality bias by
training a classifier on top of a frozen PLM on non-nationality sentiment
detection. We find consistent patterns of nationality bias across monolingual
PLMs in six languages that align with historical and political context. We also
show for English BERT that bias surfaced by LABDet correlates well with bias in
the pretraining data; thus, our work is one of the few studies that directly
links pretraining data to PLM behavior. Finally, we verify LABDet's reliability
and applicability to different templates and languages through an extensive set
of robustness checks. We publicly share our code and dataset in
https://github.com/akoksal/LABDet.Comment: EMNLP 2023 Finding
Peroxisome Proliferator-Activated Receptor alpha (PPAR alpha) down-regulation in cystic fibrosis lymphocytes
Background: PPARs exhibit anti-inflammatory capacities and are potential modulators of the inflammatory response. We hypothesized that their expression and/or function may be altered in cystic fibrosis (CF), a disorder characterized by an excessive host inflammatory response.
Methods: PPARα, β and γ mRNA levels were measured in peripheral blood cells of CF patients and healthy subjects via RT-PCR. PPARα protein expression and subcellular localization was determined via western blot and immunofluorescence, respectively. The activity of PPARα was analyzed by gel shift assay.
Results: In lymphocytes, the expression of PPARα mRNA, but not of PPARβ, was reduced (-37%; p < 0.002) in CF patients compared with healthy persons and was therefore further analyzed. A similar reduction of PPARα was observed at protein level (-26%; p < 0.05). The transcription factor was mainly expressed in the cytosol of lymphocytes, with low expression in the nucleus. Moreover, DNA binding activity of the transcription factor was 36% less in lymphocytes of patients (p < 0.01). For PPARα and PPARβ mRNA expression in monocytes and neutrophils, no significant differences were observed between CF patients and healthy persons. In all cells, PPARγ mRNA levels were below the detection limit.
Conclusion: Lymphocytes are important regulators of the inflammatory response by releasing cytokines and antibodies. The diminished lymphocytic expression and activity of PPARα may therefore contribute to the inflammatory processes that are observed in CF
Nuclear receptors in vascular biology
Nuclear receptors sense a wide range of steroids and hormones (estrogens, progesterone, androgens, glucocorticoid, and mineralocorticoid), vitamins (A and D), lipid metabolites, carbohydrates, and xenobiotics. In response to these diverse but critically important mediators, nuclear receptors regulate the homeostatic control of lipids, carbohydrate, cholesterol, and xenobiotic drug metabolism, inflammation, cell differentiation and development, including vascular development. The nuclear receptor family is one of the most important groups of signaling molecules in the body and as such represent some of the most important established and emerging clinical and therapeutic targets. This review will highlight some of the recent trends in nuclear receptor biology related to vascular biology
Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV
The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8 TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum
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