3,887 research outputs found
Reasoning on Efficient Knowledge Paths:Knowledge Graph Guides Large Language Model for Domain Question Answering
Large language models (LLMs), such as GPT3.5, GPT4 and LLAMA2 perform
surprisingly well and outperform human experts on many tasks. However, in many
domain-specific evaluations, these LLMs often suffer from hallucination
problems due to insufficient training of relevant corpus. Furthermore,
fine-tuning large models may face problems such as the LLMs are not open source
or the construction of high-quality domain instruction is difficult. Therefore,
structured knowledge databases such as knowledge graph can better provide
domain background knowledge for LLMs and make full use of the reasoning and
analysis capabilities of LLMs. In some previous works, LLM was called multiple
times to determine whether the current triplet was suitable for inclusion in
the subgraph when retrieving subgraphs through a question. Especially for the
question that require a multi-hop reasoning path, frequent calls to LLM will
consume a lot of computing power. Moreover, when choosing the reasoning path,
LLM will be called once for each step, and if one of the steps is selected
incorrectly, it will lead to the accumulation of errors in the following steps.
In this paper, we integrated and optimized a pipeline for selecting reasoning
paths from KG based on LLM, which can reduce the dependency on LLM. In
addition, we propose a simple and effective subgraph retrieval method based on
chain of thought (CoT) and page rank which can returns the paths most likely to
contain the answer. We conduct experiments on three datasets: GenMedGPT-5k
[14], WebQuestions [2], and CMCQA [21]. Finally, RoK can demonstrate that using
fewer LLM calls can achieve the same results as previous SOTAs models
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
Earth system forecasting has traditionally relied on complex physical models
that are computationally expensive and require significant domain expertise. In
the past decade, the unprecedented increase in spatiotemporal Earth observation
data has enabled data-driven forecasting models using deep learning techniques.
These models have shown promise for diverse Earth system forecasting tasks but
either struggle with handling uncertainty or neglect domain-specific prior
knowledge, resulting in averaging possible futures to blurred forecasts or
generating physically implausible predictions. To address these limitations, we
propose a two-stage pipeline for probabilistic spatiotemporal forecasting: 1)
We develop PreDiff, a conditional latent diffusion model capable of
probabilistic forecasts. 2) We incorporate an explicit knowledge control
mechanism to align forecasts with domain-specific physical constraints. This is
achieved by estimating the deviation from imposed constraints at each denoising
step and adjusting the transition distribution accordingly. We conduct
empirical studies on two datasets: N-body MNIST, a synthetic dataset with
chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset.
Specifically, we impose the law of conservation of energy in N-body MNIST and
anticipated precipitation intensity in SEVIR. Experiments demonstrate the
effectiveness of PreDiff in handling uncertainty, incorporating domain-specific
prior knowledge, and generating forecasts that exhibit high operational
utility.Comment: Technical repor
A Hybrid 3D/2D Field Response Calculation for Liquid Argon Detectors with PCB Based Anode Plane
Liquid Argon Time Projection Chamber (LArTPC) technology is commonly utilized
in neutrino detector designs. It enables detailed reconstruction of neutrino
events with high spatial precision and low energy threshold. Its field response
(FR) model describes the time-dependent electric currents induced in the
anode-plane electrodes when ionization electrons drift nearby. An accurate and
precise FR is a crucial input to LArTPC detector simulations and charge
reconstruction. Established LArTPC designs have been based on parallel wire
planes. It allows accurate and computationally economic two-dimensional (2D) FR
models utilizing the translational symmetry along the direction of the wires.
Recently, novel LArTPC designs utilize electrodes formed on printed circuit
board (PCB) in the shape of strips with through holes. The translational
symmetry is no longer a good approximation near the electrodes and a new FR
calculation that employs regions with three dimensions (3D) has been developed.
Extending the 2D models to 3D would be computationally expensive. Fortuitously,
the nature of strips with through holes allows for a computationally economic
approach based on the finite-difference method (FDM). In this paper, we present
a new software package "pochoir" that calculates LArTPC field response for
these new strip-based anode designs. This package combines 3D calculations in
the volume near the electrodes with 2D far-field solutions to achieve fast and
precise field response computation. We apply the resulting FR to simulate and
reconstruct samples of cosmic-ray muons and Ar decays from a Vertical
Drift (VD) detector prototype operated at CERN. We find the difference between
real and simulated data within 5 %. Current state-of-the-art LArTPC software
requires a 2D FR which we provide by averaging over one dimension and estimate
that variations lost in this average are smaller than 7 %.Comment: 16 pages, 12 figure
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Proteome-wide association studies for blood lipids and comparison with transcriptome-wide association studies
Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWASs) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWASs) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWASs) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWASs and TWASs can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p values across all the genes, which suggests high-level consistency between proteome-lipid associations and transcriptome-lipid associations
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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