699 research outputs found
Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition
Capturing the dependencies between joints is critical in skeleton-based
action recognition task. Transformer shows great potential to model the
correlation of important joints. However, the existing Transformer-based
methods cannot capture the correlation of different joints between frames,
which the correlation is very useful since different body parts (such as the
arms and legs in "long jump") between adjacent frames move together. Focus on
this problem, A novel spatio-temporal tuples Transformer (STTFormer) method is
proposed. The skeleton sequence is divided into several parts, and several
consecutive frames contained in each part are encoded. And then a
spatio-temporal tuples self-attention module is proposed to capture the
relationship of different joints in consecutive frames. In addition, a feature
aggregation module is introduced between non-adjacent frames to enhance the
ability to distinguish similar actions. Compared with the state-of-the-art
methods, our method achieves better performance on two large-scale datasets.Comment: 14 pages, 5 figure
Adaptive transmission in heterogeneous networks
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166243/1/cmu2bf00018.pd
Verrucisidinol and Verrucosidinol Acetate, Two Pyrone-Type Polyketides Isolated from a Marine Derived Fungus, Penicillium aurantiogriseum
The new secondary metabolites verrucosidinol (1) and its derivative verrucosidinol acetate (2), together with a potent neurotoxin verrucosidin (3), a congener norverrucosidin (4) and a mixture of two known phytotoxic metabolites terrestric acids (5 and 6), were isolated from the marine derived fungus Penicillium aurantiogriseum. Verrucosidinol has a ring-opened ethylene oxide moiety in the polyene α-pyrone skeleton, and verrucosidinol acetate is its acetate derivative. The chemical structures were determined by comparing with literature data and a combination of spectroscopic techniques, including high resolution mass spectrum and two-dimentional nuclear magnetic resonance spectroscopic analysis
Taiji Data Challenge for Exploring Gravitational Wave Universe
The direct observation of gravitational waves (GWs) opens a new window for
exploring new physics from quanta to cosmos and provides a new tool for probing
the evolution of universe. GWs detection in space covers a broad spectrum
ranging over more than four orders of magnitude and enables us to study rich
physical and astronomical phenomena. Taiji is a proposed space-based GW
detection mission that will be launched in the 2030s. Taiji will be exposed to
numerous overlapping and persistent GW signals buried in the foreground and
background, posing various data analysis challenges. In order to empower
potential scientific discoveries, the Mock LISA Data Challenge and the LISA
Data Challenge (LDC) were developed. While LDC provides a baseline framework,
the first LDC needs to be updated with more realistic simulations and adjusted
detector responses for Taiji's constellation. In this paper, we review the
scientific objectives and the roadmap for Taiji, as well as the technical
difficulties in data analysis and the data generation strategy, and present the
associated data challenges. In contrast to LDC, we utilize second-order
Keplerian orbit and second-generation time delay interferometry techniques.
Additionally, we employ a new model for the extreme-mass-ratio inspiral
waveform and stochastic GW background spectrum, which enables us to test
general relativity and measure the non-Gaussianity of curvature perturbations.
Furthermore, we present a comprehensive showcase of parameter estimation using
a toy dataset. This showcase not only demonstrates the scientific potential of
the Taiji Data Challenge but also serves to validate the effectiveness of the
pipeline. As the first data challenge for Taiji, we aim to build an open ground
for data analysis related to Taiji sources and sciences. More details can be
found on the official website at http://taiji-tdc.ictp-ap.org.Comment: 15 pages, 3 figure
Retrieving Soil and Vegetation Temperatures From Dual-Angle and Multipixel Satellite Observations
Land surface component temperatures (LSCTs), i.e., the temperatures of soil and vegetation, are important parameters in many applications, such as estimating evapotranspiration and monitoring droughts. However, the multiangle algorithm is affected due to different spatial resolution between nadir and oblique views. Therefore, we propose a combined retrieval algorithm that uses dual-angle and multipixel observations together. The sea and land surface temperature radiometer onboard ESA\u27s Sentinel-3 satellite allows for quasi-synchronous dual-angle observations, from which LSCTs can be retrieved using dual-angle and multipixel algorithms. The better performance of the combined algorithm is demonstrated using a sensitivity analysis based on a synthetic dataset. The spatial errors in the oblique view due to different spatial resolution can reach 4.5 K and have a large effect on the multiangle algorithm. The introduction of multipixel information in a window can reduce the effect of such spatial errors, and the retrieval results of LSCTs can be further improved by using multiangle information for a pixel. In the validation, the proposed combined algorithm performed better, with LSCT root mean squared errors of 3.09 K and 1.91 K for soil and vegetation at a grass site, respectively, and corresponding values of 3.71 K and 3.42 K at a sparse forest site, respectively. Considering that the temperature differences between components can reach 20 K, the results confirm that, in addition to a pixel-average LST, the combined retrieval algorithm can provide information on LSCTs. This article demonstrates the potential of utilizing additional information sources for better LSCT results, which makes the presented combined strategy a promising option for deriving large-scale LSCT products
Leveraging GPT-4 for Food Effect Summarization to Enhance Product-Specific Guidance Development via Iterative Prompting
Food effect summarization from New Drug Application (NDA) is an essential
component of product-specific guidance (PSG) development and assessment.
However, manual summarization of food effect from extensive drug application
review documents is time-consuming, which arouses a need to develop automated
methods. Recent advances in large language models (LLMs) such as ChatGPT and
GPT-4, have demonstrated great potential in improving the effectiveness of
automated text summarization, but its ability regarding the accuracy in
summarizing food effect for PSG assessment remains unclear. In this study, we
introduce a simple yet effective approach, iterative prompting, which allows
one to interact with ChatGPT or GPT-4 more effectively and efficiently through
multi-turn interaction. Specifically, we propose a three-turn iterative
prompting approach to food effect summarization in which the keyword-focused
and length-controlled prompts are respectively provided in consecutive turns to
refine the quality of the generated summary. We conduct a series of extensive
evaluations, ranging from automated metrics to FDA professionals and even
evaluation by GPT-4, on 100 NDA review documents selected over the past five
years. We observe that the summary quality is progressively improved throughout
the process. Moreover, we find that GPT-4 performs better than ChatGPT, as
evaluated by FDA professionals (43% vs. 12%) and GPT-4 (64% vs. 35%).
Importantly, all the FDA professionals unanimously rated that 85% of the
summaries generated by GPT-4 are factually consistent with the golden reference
summary, a finding further supported by GPT-4 rating of 72% consistency. These
results strongly suggest a great potential for GPT-4 to draft food effect
summaries that could be reviewed by FDA professionals, thereby improving the
efficiency of PSG assessment cycle and promoting the generic drug product
development.Comment: 22 pages, 6 figure
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