111 research outputs found
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective
We investigate the problem of learning with noisy labels in real-world
annotation scenarios, where noise can be categorized into two types: factual
noise and ambiguity noise. To better distinguish these noise types and utilize
their semantics, we propose a novel sample selection-based approach for noisy
label learning, called Proto-semi. Proto-semi initially divides all samples
into the confident and unconfident datasets via warm-up. By leveraging the
confident dataset, prototype vectors are constructed to capture class
characteristics. Subsequently, the distances between the unconfident samples
and the prototype vectors are calculated to facilitate noise classification.
Based on these distances, the labels are either corrected or retained,
resulting in the refinement of the confident and unconfident datasets. Finally,
we introduce a semi-supervised learning method to enhance training. Empirical
evaluations on a real-world annotated dataset substantiate the robustness of
Proto-semi in handling the problem of learning from noisy labels. Meanwhile,
the prototype-based repartitioning strategy is shown to be effective in
mitigating the adverse impact of label noise. Our code and data are available
at https://github.com/fuxiAIlab/ProtoSemi
FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models
Collecting high-quality labeled data for model training is notoriously
time-consuming and labor-intensive for various NLP tasks. While copious
solutions, such as active learning for small language models (SLMs) and
prevalent in-context learning in the era of large language models (LLMs), have
been proposed and alleviate the labeling burden to some extent, their
performances are still subject to human intervention. It is still underexplored
how to reduce the annotation cost in the LLMs era. To bridge this, we
revolutionize traditional active learning and propose an innovative
collaborative learning framework FreeAL to interactively distill and filter the
task-specific knowledge from LLMs. During collaborative training, an LLM serves
as an active annotator inculcating its coarse-grained knowledge, while a
downstream SLM is incurred as a student to filter out high-quality in-context
samples to feedback LLM for the subsequent label refinery. Extensive
experiments on eight benchmark datasets demonstrate that FreeAL largely
enhances the zero-shot performances for both SLM and LLM without any human
supervision. The code is available at https://github.com/Justherozen/FreeAL .Comment: Accepted to EMNLP 2023 (Main conference
DeepFlame: A deep learning empowered open-source platform for reacting flow simulations
In this work, we introduce DeepFlame, an open-source C++ platform with the
capabilities of utilising machine learning algorithms and pre-trained models to
solve for reactive flows. We combine the individual strengths of the
computational fluid dynamics library OpenFOAM, machine learning framework
Torch, and chemical kinetics program Cantera. The complexity of cross-library
function and data interfacing (the core of DeepFlame) is minimised to achieve a
simple and clear workflow for code maintenance, extension and upgrading. As a
demonstration, we apply our recent work on deep learning for predicting
chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to
highlight the potential of machine learning in accelerating reacting flow
simulation. A thorough code validation is conducted via a broad range of
canonical cases to assess its accuracy and efficiency. The results demonstrate
that the convection-diffusion-reaction algorithms implemented in DeepFlame are
robust and accurate for both steady-state and transient processes. In addition,
a number of methods aiming to further improve the computational efficiency,
e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their
performances are also evaluated and reported. With the deep learning method
implemented in this work, a speed-up of two orders of magnitude is achieved in
a simple hydrogen ignition case when performed on a medium-end graphics
processing unit (GPU). Further gain in computational efficiency is expected for
hydrocarbon and other complex fuels. A similar level of acceleration is
obtained on an AI-specific chip - deep computing unit (DCU), highlighting the
potential of DeepFlame in leveraging the next-generation computing architecture
and hardware
A novel method of weakness imbalance fault identification and application in aero-hydraulic pump
A method of combining auto-correlation and Hilbert envelope analysis is proposed and used to identify weakness imbalance fault of aero-hydraulic pump, the central part of hydraulic system of aircraft. Firstly, the integral and polynomial least square fitting is applied to convert acceleration signal to velocity one; secondly, the Hilbert envelope spectrum of auto-correlation function of velocity signal is obtained and used to identify the weakness imbalance fault of aero-hydraulic pump; finally, the energy ratio of velocity signal is calculated according to Hilbert envelope spectrum for identifying imbalance fault of aero-hydraulic pump by means of easier and more visual method. Meanwhile, the comparing analysis is carried out between traditional research method and proposed new one. The result shows that the weakness imbalance fault of aero-hydraulic pump can be identified and diagnosed effectively and correctly according to the velocity signal whether Hilbert envelope spectrum or calculation energy ratio while direct acceleration signal cannot
Towards Long-term Annotators: A Supervised Label Aggregation Baseline
Relying on crowdsourced workers, data crowdsourcing platforms are able to
efficiently provide vast amounts of labeled data. Due to the variability in the
annotation quality of crowd workers, modern techniques resort to redundant
annotations and subsequent label aggregation to infer true labels. However,
these methods require model updating during the inference, posing challenges in
real-world implementation. Meanwhile, in recent years, many data labeling tasks
have begun to require skilled and experienced annotators, leading to an
increasing demand for long-term annotators. These annotators could leave
substantial historical annotation records on the crowdsourcing platforms, which
can benefit label aggregation, but are ignored by previous works. Hereby, in
this paper, we propose a novel label aggregation technique, which does not need
any model updating during inference and can extensively explore the historical
annotation records. We call it SuperLA, a Supervised Label Aggregation method.
Inside this model, we design three types of input features and a
straightforward neural network structure to merge all the information together
and subsequently produce aggregated labels. Based on comparison experiments
conducted on 22 public datasets and 11 baseline methods, we find that SuperLA
not only outperforms all those baselines in inference performance but also
offers significant advantages in terms of efficiency
Fabrication and characteristics of flexible normally-off AlGaN/GaN HEMTs
In this paper, we present a method for removing a high electron mobility transistor (HEMT) silicon substrate using mechanical grinding and deep silicon etching technology and successfully transferred the epitaxial wafer to a PET substrate to achieve the flexible normally-off HEMT. By testing the output characteristics and transfer characteristics of the Si-substrate HEMT and PET-substrate HEMT, we have demonstrated that the PET-substrate HEMT has excellent performance and successfully achieved the mechanical flexibility. Furthermore, we analyzed the physical mechanisms of the change in PET-substrate and Si-substrate HEMT characteristics, as well as flexible HEMT performance under bent and flattened states. The flexible HEMT array demonstrates significant potential in integration with other flexible devices, such as GaN-based micro-LED arrays
The association Between Short-Term Emotion Dynamics and Cigarette Dependence: a Comprehensive Examination of Dynamic Measures
BACKGROUND: The association between short-term emotion dynamics and long-term psychopathology has been well established in the psychology literature. Yet, dynamic measures for inertia and instability of negative and positive affect have not been studied in terms of their association with cigarette dependence. This study builds an important bridge between the psychology and substance use literatures by introducing these novel measures and conducting a comprehensive examination of such association with intervention implications.
METHODS: This study conducted secondary analysis on the data from a community sample of 136 dual users (e-cigarette + cigarette) and 101 exclusive smokers who completed both the two-week ecological momentary assessment (EMA) and cigarette dependence assessments in a recent study.
RESULTS: Among dual users, a higher average level of negative affect, lower inertia of negative affect (i.e., less sustained negative affect), and higher instability of positive affect (i.e., greater magnitude of changes in positive affect) were associated with higher cigarette dependence. The patterns of associations among exclusive smokers were, however, different. Higher inertia of negative affect, lower instability of positive affect, and higher variability of negative affect were associated with higher dependence.
CONCLUSIONS: The results illustrate the importance of examining not only negative affect but also positive affect in order to fully understand the association between emotion dynamics and cigarette dependence. The different patterns of association between emotion dynamics and cigarette dependence across the two groups of cigarette users also call for future research that is designed to compare cigarettes and e-cigarettes in terms of their effects on emotion regulation
FADTTS: Functional analysis of diffusion tensor tract statistics
The aim of this paper is to present a functional analysis of diffusion tensor tract statistics (FADTTS) pipeline for delineating the association between multiple diffusion properties along major white matter fiber bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these white matter tract properties in various diffusion tensor imaging studies. The FADTTS integrates five statistical tools: (i) a multivariate varying coefficient model for allowing the varying coefficient functions in terms of arc length to characterize the varying association between fiber bundle diffusion properties and a set of covariates, (ii) a weighted least squares estimation of the varying coefficient functions, (iii) a functional principal component analysis to delineate the structure of the variability in fiber bundle diffusion properties, (iv) a global test statistic to test hypotheses of interest, which may be associated with different diffusion properties, and (v) a simultaneous confidence band to quantify the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FADTTS. We also apply FADTTS to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment. FADTTS can be used to facilitate understanding of normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles
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