244 research outputs found
Towards Free Data Selection with General-Purpose Models
A desirable data selection algorithm can efficiently choose the most
informative samples to maximize the utility of limited annotation budgets.
However, current approaches, represented by active learning methods, typically
follow a cumbersome pipeline that iterates the time-consuming model training
and batch data selection repeatedly. In this paper, we challenge this status
quo by designing a distinct data selection pipeline that utilizes existing
general-purpose models to select data from various datasets with a single-pass
inference without the need for additional training or supervision. A novel free
data selection (FreeSel) method is proposed following this new pipeline.
Specifically, we define semantic patterns extracted from inter-mediate features
of the general-purpose model to capture subtle local information in each image.
We then enable the selection of all data samples in a single pass through
distance-based sampling at the fine-grained semantic pattern level. FreeSel
bypasses the heavy batch selection process, achieving a significant improvement
in efficiency and being 530x faster than existing active learning methods.
Extensive experiments verify the effectiveness of FreeSel on various computer
vision tasks. Our code is available at https://github.com/yichen928/FreeSel.Comment: accepted by NeurIPS 202
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
Traffic signal control is an emerging application scenario for reinforcement
learning. Besides being as an important problem that affects people's daily
life in commuting, traffic signal control poses its unique challenges for
reinforcement learning in terms of adapting to dynamic traffic environment and
coordinating thousands of agents including vehicles and pedestrians. A key
factor in the success of modern reinforcement learning relies on a good
simulator to generate a large number of data samples for learning. The most
commonly used open-source traffic simulator SUMO is, however, not scalable to
large road network and large traffic flow, which hinders the study of
reinforcement learning on traffic scenarios. This motivates us to create a new
traffic simulator CityFlow with fundamentally optimized data structures and
efficient algorithms. CityFlow can support flexible definitions for road
network and traffic flow based on synthetic and real-world data. It also
provides user-friendly interface for reinforcement learning. Most importantly,
CityFlow is more than twenty times faster than SUMO and is capable of
supporting city-wide traffic simulation with an interactive render for
monitoring. Besides traffic signal control, CityFlow could serve as the base
for other transportation studies and can create new possibilities to test
machine learning methods in the intelligent transportation domain.Comment: WWW 2019 Demo Pape
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL
task. However, the absence of a systematical benchmark inhibits the development
of designing effective, efficient and economic LLM-based Text-to-SQL solutions.
To address this challenge, in this paper, we first conduct a systematical and
extensive comparison over existing prompt engineering methods, including
question representation, example selection and example organization, and with
these experimental results, we elaborate their pros and cons. Based on these
findings, we propose a new integrated solution, named DAIL-SQL, which refreshes
the Spider leaderboard with 86.6% execution accuracy and sets a new bar. To
explore the potential of open-source LLM, we investigate them in various
scenarios, and further enhance their performance with supervised fine-tuning.
Our explorations highlight open-source LLMs' potential in Text-to-SQL, as well
as the advantages and disadvantages of the supervised fine-tuning.
Additionally, towards an efficient and economic LLM-based Text-to-SQL solution,
we emphasize the token efficiency in prompt engineering and compare the prior
studies under this metric. We hope that our work provides a deeper
understanding of Text-to-SQL with LLMs, and inspires further investigations and
broad applications.Comment: We have released code on https://github.com/BeachWang/DAIL-SQ
Simplified three-dimensional tissue clearing and incorporation of colorimetric phenotyping.
Tissue clearing methods promise to provide exquisite three-dimensional imaging information; however, there is a need for simplified methods for lower resource settings and for non-fluorescence based phenotyping to enable light microscopic imaging modalities. Here we describe the simplified CLARITY method (SCM) for tissue clearing that preserves epitopes of interest. We imaged the resulting tissues using light sheet microscopy to generate rapid 3D reconstructions of entire tissues and organs. In addition, to enable clearing and 3D tissue imaging with light microscopy methods, we developed a colorimetric, non-fluorescent method for specifically labeling cleared tissues based on horseradish peroxidase conversion of diaminobenzidine to a colored insoluble product. The methods we describe here are portable and can be accomplished at low cost, and can allow light microscopic imaging of cleared tissues, thus enabling tissue clearing and imaging in a wide variety of settings
Aluminum Complexes of N2O23‒ Formazanate Ligands Supported by Phosphine Oxide Donors
The synthesis and characterization of a new family of phosphine-oxide-supported aluminum formazanate complexes (7a, 7b, 8a, 9a) are reported. X-ray diffraction studies revealed that the aluminum atoms in the complexes adopt an octahedral geometry in the solid state. The equatorial positions are occupied by an N2O23‒ formazanate ligand, and the axial positions are occupied by L-type phosphine oxide donors. UV-vis absorption spectroscopy revealed that the complexes were strongly absorbing (ε ~ 30,000 M‒1 cm‒1) between 500 and 700 nm. The absorption maxima in this region were simulated using time-dependent density-functional theory. With the exception of 3-cyano substituted complex 7b, which showed maximum luminescence intensity in the presence of excess phosphine oxide, the title complexes are non-emissive in solution and the solid state. The electrochemical properties of the complexes were probed using cyclic voltammetry. Each complex underwent sequential one-electron oxidations in potential ranges of ‒0.12 to 0.29 V and 0.62 to 0.97 V, relative to the ferrocene/ferrocenium redox couple. Electrochemical reduction events were observed at potentials between ‒1.34 and ‒1.75 V. When combined with tri-n-propylamine as a coreactant, complex 7b acted as an electrochemiluminescence emitter with a maximum electrochemiluminescence intensity at a wavelength of 735 nm, red-shifted relative to the photoluminescence maximum of the same compound
Advanced microscopy to elucidate cardiovascular injury and regeneration: 4D light-sheet imaging
The advent of 4-dimensional (4D) light-sheet fluorescence microscopy (LSFM) has provided an entry point for rapid image acquisition to uncover real-time cardiovascular structure and function with high axial resolution and minimal photo-bleaching/-toxicity. We hereby review the fundamental principles of our LSFM system to investigate cardiovascular morphogenesis and regeneration after injury. LSFM enables us to reveal the micro-circulation of blood cells in the zebrafish embryo and assess cardiac ventricular remodeling in response to chemotherapy-induced injury using an automated segmentation approach. Next, we review two distinct mechanisms underlying zebrafish vascular regeneration following tail amputation. We elucidate the role of endothelial Notch signaling to restore vascular regeneration after exposure to the redox active ultrafine particles (UFP) in air pollutants. By manipulating the blood viscosity and subsequently, endothelial wall shear stress, we demonstrate the mechanism whereby hemodynamic shear forces impart both mechanical and metabolic effects to modulate vascular regeneration. Overall, the implementation of 4D LSFM allows for the elucidation of mechanisms governing cardiovascular injury and regeneration with high spatiotemporal resolution
Modeling of the Rating of Perceived Exertion Based on Heart Rate Using Machine Learning Methods
Abstract Rating of perceived exertion (RPE) can serve as a more convenient and economical alternative to heart rate (HR) for exercise intensity control. This study aims to explore the influence of factors, such as indicators of demographic, anthropometric, body composition, cardiovascular function and basic exercise ability on the relationship between HR and RPE, and to develop the model predicting RPE from HR. 48 healthy participants were recruited to perform an incrementally 6-stage pedaling test. HR and RPE were collected during each stage. The influencing factors were identified with the forward selection method to train Gaussian Process regression (GPR), support vector machine (SVM) and linear regression models. Metrics of R2, adjusted R2 and RMSE were calculated to evaluate the performance of the models. The GPR model outperformed the SVM and linear regression models, and achieved an R2 of 0.95, adjusted R2 of 0.89 and RMSE of 0.52. Indicators of age, resting heart rate (RHR), Central arterial pressure (CAP), body fat rate (BFR) and body mass index (BMI) were identified as factors that best predicted the relationship between RPE and HR. It is possible to use GPR model to estimate RPE from HR accurately, after adjusting for age, RHR, CAP, BFR and BMI
Simulating Developmental Cardiac Morphology in Virtual Reality Using a Deformable Image Registration Approach
While virtual reality (VR) has potential in enhancing cardiovascular diagnosis and treatment, prerequisite labor-intensive image segmentation remains an obstacle for seamlessly simulating 4-dimensional (4-D, 3-D + time) imaging data in an immersive, physiological VR environment. We applied deformable image registration (DIR) in conjunction with 3-D reconstruction and VR implementation to recapitulate developmental cardiac contractile function from light-sheet fluorescence microscopy (LSFM). This method addressed inconsistencies that would arise from independent segmentations of time-dependent data, thereby enabling the creation of a VR environment that fluently simulates cardiac morphological changes. By analyzing myocardial deformation at high spatiotemporal resolution, we interfaced quantitative computations with 4-D VR. We demonstrated that our LSFM-captured images, followed by DIR, yielded average dice similarity coefficients of 0.92 ± 0.05 (n = 510) and 0.93 ± 0.06 (n = 240) when compared to ground truth images obtained from Otsu thresholding and manual segmentation, respectively. The resulting VR environment simulates a wide-angle zoomed-in view of motion in live embryonic zebrafish hearts, in which the cardiac chambers are undergoing structural deformation throughout the cardiac cycle. Thus, this technique allows for an interactive micro-scale VR visualization of developmental cardiac morphology to enable high resolution simulation for both basic and clinical science
Association between weight-adjusted-waist index and the risk of hyperuricemia in adults: a population-based investigation
ObjectiveThis investigation sought to elucidate the potential correlation between a recently characterized adiposity metric, termed the Weight-Adjusted-Waist Index (WWI) and hyperuricemia.MethodsA cross-sectional design was employed in this study, featuring both hyperuricemic and non-hyperuricemic subjects with complete WWI data, sourced from the National Health and Nutrition Examination Survey (NHANES) spanning 2017 to March 2020. WWI was calculated utilizing the formula which involves the division of waist circumference (WC) by the square root of the body weight. In order to determine the relationship between WWI and hyperuricemia, both univariate and multivariate logistic regression models, appropriately weighted, were employed in the analysis. The linearity of relationships was validated using smooth curve fitting. Additionally, subgroup evaluations and interaction assessments were conducted.ResultsThe study sample comprised 7437 subjects, yielding a hyperuricemia prevalence of 18.22%. Stratifying WWI into tertiles, a progressive rise in hyperuricemia prevalence was evident with increasing WWI (Tertile 1: 11.62%, Tertile 2: 17.91%, Tertile 3: 25.13%). The odds ratio (OR) demonstrated that individuals within the highest WWI tertile were significantly more prone to hyperuricemia than those in the lowest tertile (OR = 2.41, 95% CI: 1.88-3.08).ConclusionThis study provides evidence that an elevated WWI is correlated with an increased risk of hyperuricemia in the adult population of the United States. These results suggest that WWI may serve as a viable anthropometric indicator for predicting hyperuricemia
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