88 research outputs found
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Scientists' political behaviors are not driven by individual-level government benefits.
Is it appropriate for scientists to engage in political advocacy? Some political critics of scientists argue that scientists have become partisan political actors with self-serving financial agendas. However, most scientists strongly reject this view. While social scientists have explored the effects of science politicization on public trust in science, little empirical work directly examines the drivers of scientists' interest in and willingness to engage in political advocacy. Using a natural experiment involving the U.S. National Science Foundation Graduate Research Fellowship (NSF-GRF), we causally estimate for the first time whether scientists who have received federal science funding are more likely to engage in both science-related and non-science-related political behaviors. Comparing otherwise similar individuals who received or did not receive NSF support, we find that scientists' preferences for political advocacy are not shaped by receiving government benefits. Government funding did not impact scientists' support of the 2017 March for Science nor did it shape the likelihood that scientists donated to either Republican or Democratic political groups. Our results offer empirical evidence that scientists' political behaviors are not motivated by self-serving financial agendas. They also highlight the limited capacity of even generous government support programs to increase civic participation by their beneficiaries
On the Pareto Front of Multilingual Neural Machine Translation
In this work, we study how the performance of a given direction changes with
its sampling ratio in Multilingual Neural Machine Translation (MNMT). By
training over 200 multilingual models with various model sizes, data sizes, and
language directions, we find it interesting that the performance of certain
translation direction does not always improve with the increase of its weight
in the multi-task optimization objective. Accordingly, scalarization method
leads to a multitask trade-off front that deviates from the traditional Pareto
front when there exists data imbalance in the training corpus, which poses a
great challenge to improve the overall performance of all directions. Based on
our observations, we propose the Double Power Law to predict the unique
performance trade-off front in MNMT, which is robust across various languages,
data adequacy, and the number of tasks. Finally, we formulate the sample ratio
selection problem in MNMT as an optimization problem based on the Double Power
Law. In our experiments, it achieves better performance than temperature
searching and gradient manipulation methods with only 1/5 to 1/2 of the total
training budget. We release the code at
https://github.com/pkunlp-icler/ParetoMNMT for reproduction.Comment: NeurIPS 202
Revisiting IP-to-AS mapping for AS-level traceroute
ABSTRACT On the way to obtaining accurate AS-level traceroute paths, lots of efforts have focused on the improvement of the original IP-to-AS mapping table which was extracted from BGP routing tables. One of those efforts is called pair matching which refines the original mapping table by maximizing the number of matched pairs of traceroute and BGP AS paths from the same AS to the same destination. However, in the existing pair-matching-based method, the granularity for mapping is prefix, i.e. that the IP addresses in the same /24 prefix always belong to the same AS or set of ASes, which may yield ambiguity and does not hold in some cases. In this paper, we revisit the IP-to-AS mapping with IP-address granularity by allowing IP addresses in the same prefix to be mapped to different ASes
Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and Beyond
In this study, we explore the potential of Multimodal Large Language Models
(MLLMs) in improving embodied decision-making processes for agents. While Large
Language Models (LLMs) have been widely used due to their advanced reasoning
skills and vast world knowledge, MLLMs like GPT4-Vision offer enhanced visual
understanding and reasoning capabilities. We investigate whether
state-of-the-art MLLMs can handle embodied decision-making in an end-to-end
manner and whether collaborations between LLMs and MLLMs can enhance
decision-making. To address these questions, we introduce a new benchmark
called PCA-EVAL, which evaluates embodied decision-making from the perspectives
of Perception, Cognition, and Action. Additionally, we propose HOLMES, a
multi-agent cooperation framework that allows LLMs to leverage MLLMs and APIs
to gather multimodal information for informed decision-making. We compare
end-to-end embodied decision-making and HOLMES on our benchmark and find that
the GPT4-Vision model demonstrates strong end-to-end embodied decision-making
abilities, outperforming GPT4-HOLMES in terms of average decision accuracy
(+3%). However, this performance is exclusive to the latest GPT4-Vision model,
surpassing the open-source state-of-the-art MLLM by 26%. Our results indicate
that powerful MLLMs like GPT4-Vision hold promise for decision-making in
embodied agents, offering new avenues for MLLM research. Code and data are open
at https://github.com/pkunlp-icler/PCA-EVAL/.Comment: FMDM@NeurIPS2023, Code and data:
https://github.com/pkunlp-icler/PCA-EVAL
Combined linkage and association mapping reveals candidates for Scmv1, a major locus involved in resistance to sugarcane mosaic virus (SCMV) in maize
Background
Sugarcane mosaic virus (SCMV) disease causes substantial losses of grain yield and forage biomass in susceptible maize cultivars. Maize resistance to SCMV is associated with two dominant genes, Scmv1 and Scmv2, which are located on the short arm of chromosome 6 and near the centromere region of chromosome 3, respectively. We combined both linkage and association mapping to identify positional candidate genes for Scmv1. Results
Scmv1 was fine-mapped in a segregating population derived from near-isogenic lines and further validated and fine-mapped using two recombinant inbred line populations. The combined results assigned the Scmv1 locus to a 59.21-kb interval, and candidate genes within this region were predicted based on the publicly available B73 sequence. None of three predicted genes that are possibly involved in the disease resistance response are similar to receptor-like resistance genes. Candidate gene–based association mapping was conducted using a panel of 94 inbred lines with variable resistance to SCMV. A presence/absence variation (PAV) in the Scmv1 region and two polymorphic sites around the Zmtrx-h gene were significantly associated with SCMV resistance. Conclusion
Combined linkage and association mapping pinpoints Zmtrx-h as the most likely positional candidate gene for Scmv1. These results pave the way towards cloning of Scmv1 and facilitate marker-assisted selection for potyvirus resistance in maize
Is My Prediction Arbitrary? The Confounding Effects of Variance in Fair Classification Benchmarks
Variance in predictions across different trained models is a significant,
under-explored source of error in fair classification. In practice, the
variance on some data examples is so large that decisions can be effectively
arbitrary. To investigate this problem, we take an experimental approach and
make four overarching contributions: We 1) Define a metric called
self-consistency, derived from variance, which we use as a proxy for measuring
and reducing arbitrariness; 2) Develop an ensembling algorithm that abstains
from classification when a prediction would be arbitrary; 3) Conduct the
largest to-date empirical study of the role of variance (vis-a-vis
self-consistency and arbitrariness) in fair classification; and, 4) Release a
toolkit that makes the US Home Mortgage Disclosure Act (HMDA) datasets easily
usable for future research. Altogether, our experiments reveal shocking
insights about the reliability of conclusions on benchmark datasets. Most
fairness classification benchmarks are close-to-fair when taking into account
the amount of arbitrariness present in predictions -- before we even try to
apply common fairness interventions. This finding calls into question the
practical utility of common algorithmic fairness methods, and in turn suggests
that we should fundamentally reconsider how we choose to measure fairness in
machine learning
The recent progress of peptide regulators for the Wnt/β-catenin signaling pathway
Wnt signaling plays an important role in many biological processes such as stem cell self-renewal, cell proliferation, migration, and differentiation. The β-catenin-dependent signaling pathway mainly regulates cell proliferation, differentiation, and migration. In the Wnt/β-catenin signaling pathway, the Wnt family ligands transduce signals through LRP5/6 and Frizzled receptors to the Wnt/β-catenin signaling cascades. Wnt-targeted therapy has garnered extensive attention. The most commonly used approach in targeted therapy is small-molecule regulators. However, it is difficult for small-molecule regulators to make great progress due to their inherent defects. Therapeutic peptide regulators targeting the Wnt signaling pathway have become an alternative therapy, promising to fill the gaps in the clinical application of small-molecule regulators. In this review, we describe recent advances in peptide regulators for Wnt/β-catenin signaling
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