157 research outputs found
Zero-shot sampling of adversarial entities in biomedical question answering
The increasing depth of parametric domain knowledge in large language models
(LLMs) is fueling their rapid deployment in real-world applications. In
high-stakes and knowledge-intensive tasks, understanding model vulnerabilities
is essential for quantifying the trustworthiness of model predictions and
regulating their use. The recent discovery of named entities as adversarial
examples in natural language processing tasks raises questions about their
potential guises in other settings. Here, we propose a powerscaled
distance-weighted sampling scheme in embedding space to discover diverse
adversarial entities as distractors. We demonstrate its advantage over random
sampling in adversarial question answering on biomedical topics. Our approach
enables the exploration of different regions on the attack surface, which
reveals two regimes of adversarial entities that markedly differ in their
characteristics. Moreover, we show that the attacks successfully manipulate
token-wise Shapley value explanations, which become deceptive in the
adversarial setting. Our investigations illustrate the brittleness of domain
knowledge in LLMs and reveal a shortcoming of standard evaluations for
high-capacity models.Comment: 20 pages incl. appendix, under revie
Machine Learning on Neutron and X-Ray Scattering
Neutron and X-ray scattering represent two state-of-the-art materials
characterization techniques that measure materials' structural and dynamical
properties with high precision. These techniques play critical roles in
understanding a wide variety of materials systems, from catalysis to polymers,
nanomaterials to macromolecules, and energy materials to quantum materials. In
recent years, neutron and X-ray scattering have received a significant boost
due to the development and increased application of machine learning to
materials problems. This article reviews the recent progress in applying
machine learning techniques to augment various neutron and X-ray scattering
techniques. We highlight the integration of machine learning methods into the
typical workflow of scattering experiments. We focus on scattering problems
that faced challenge with traditional methods but addressable using machine
learning, such as leveraging the knowledge of simple materials to model more
complicated systems, learning with limited data or incomplete labels,
identifying meaningful spectra and materials' representations for learning
tasks, mitigating spectral noise, and many others. We present an outlook on a
few emerging roles machine learning may play in broad types of scattering and
spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom
Cell Cycle-Regulated Protein Abundance Changes in Synchronously Proliferating HeLa Cells Include Regulation of Pre-mRNA Splicing Proteins
Cell proliferation involves dramatic changes in DNA metabolism and cell division, and control of DNA replication, mitosis, and cytokinesis have received the greatest attention in the cell cycle field. To catalogue a wider range of cell cycle-regulated processes, we employed quantitative proteomics of synchronized HeLa cells. We quantified changes in protein abundance as cells actively progress from G1 to S phase and from S to G2 phase. We also describe a cohort of proteins whose abundance changes in response to pharmacological inhibition of the proteasome. Our analysis reveals not only the expected changes in proteins required for DNA replication and mitosis but also cell cycle-associated changes in proteins required for biological processes not known to be cell-cycle regulated. For example, many pre-mRNA alternative splicing proteins are down-regulated in S phase. Comparison of this dataset to several other proteomic datasets sheds light on global mechanisms of cell cycle phase transitions and underscores the importance of both phosphorylation and ubiquitination in cell cycle changes
Pairing symmetry and properties of iron-based high temperature superconductors
Pairing symmetry is important to indentify the pairing mechanism. The
analysis becomes particularly timely and important for the newly discovered
iron-based multi-orbital superconductors. From group theory point of view we
classified all pairing matrices (in the orbital space) that carry irreducible
representations of the system. The quasiparticle gap falls into three
categories: full, nodal and gapless. The nodal-gap states show conventional
Volovik effect even for on-site pairing. The gapless states are odd in orbital
space, have a negative superfluid density and are therefore unstable. In
connection to experiments we proposed possible pairing states and implications
for the pairing mechanism.Comment: 4 pages, 1 table, 2 figures, polished versio
Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis
Genetic association studies have identified 21 loci associated with atopic dermatitis risk predominantly in populations of European ancestry. To identify further susceptibility loci for this common, complex skin disease, we performed a meta-analysis of >15 million genetic variants in 21,399 cases and 95,464 controls from populations of European, African, Japanese and Latino ancestry, followed by replication in 32,059 cases and 228,628 controls from 18 studies. We identified ten new risk loci, bringing the total number of known atopic dermatitis risk loci to 31 (with new secondary signals at four of these loci). Notably, the new loci include candidate genes with roles in the regulation of innate host defenses and T cell function, underscoring the important contribution of (auto)immune mechanisms to atopic dermatitis pathogenesis
Diverse Applications of Nanomedicine
The design and use of materials in the nanoscale size range for addressing medical and health-related issues continues to receive increasing interest. Research in nanomedicine spans a multitude of areas, including drug delivery, vaccine development, antibacterial, diagnosis and imaging tools, wearable devices, implants, high-throughput screening platforms, etc. using biological, nonbiological, biomimetic, or hybrid materials. Many of these developments are starting to be translated into viable clinical products. Here, we provide an overview of recent developments in nanomedicine and highlight the current challenges and upcoming opportunities for the field and translation to the clinic. \ua9 2017 American Chemical Society
Addressing climate change with behavioral science: a global intervention tournament in 63 countries
Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors
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