589 research outputs found
Optimum Operating Conditions Confirmation and Effectiveness Analysis Based on Research of the Coagulation and Precipitation Integrated Process
AbstractAiming at the increasing small-scale water supply projects, the increasingly serious pollution of the water resource and stringent water quality standards, the coagulation and precipitation integrated process on the basis of quiescent precipitation was proposed in this study. By experiments in the integrated reactor, the optimum process operating conditions were confirmed. It is verified that the optimal dosage of PAC was 16mg/L in the optimum temperature and pH range. The repeated utilization volume of the floc mud from the former precipitation period was the same as 6% of the water volume in the next processing period, and the corresponding optimal dosage of PAC was 8mg/L with 50% reduction of the flocculants dosage, while the residual turbidity was less than 1.0NTU, which could reach the standard after simple filtration and disinfection procedure. With low energy consumption, little land occupation, low cost, high efficiency of the water production and strong anti shock loading capability, this process could guarantee the safety of drinking water supply, and deserve popularization and application
M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis
Multimodal Aspect-based Sentiment Analysis (MABSA) is a fine-grained
Sentiment Analysis task, which has attracted growing research interests
recently. Existing work mainly utilizes image information to improve the
performance of MABSA task. However, most of the studies overestimate the
importance of images since there are many noise images unrelated to the text in
the dataset, which will have a negative impact on model learning. Although some
work attempts to filter low-quality noise images by setting thresholds, relying
on thresholds will inevitably filter out a lot of useful image information.
Therefore, in this work, we focus on whether the negative impact of noisy
images can be reduced without modifying the data. To achieve this goal, we
borrow the idea of Curriculum Learning and propose a Multi-grained
Multi-curriculum Denoising Framework (M2DF), which can achieve denoising by
adjusting the order of training data. Extensive experimental results show that
our framework consistently outperforms state-of-the-art work on three sub-tasks
of MABSA.Comment: Accepted by EMNLP 202
GPT-NER: Named Entity Recognition via Large Language Models
Despite the fact that large-scale Language Models (LLM) have achieved SOTA
performances on a variety of NLP tasks, its performance on NER is still
significantly below supervised baselines. This is due to the gap between the
two tasks the NER and LLMs: the former is a sequence labeling task in nature
while the latter is a text-generation model.
In this paper, we propose GPT-NER to resolve this issue. GPT-NER bridges the
gap by transforming the sequence labeling task to a generation task that can be
easily adapted by LLMs e.g., the task of finding location entities in the input
text "Columbus is a city" is transformed to generate the text sequence
"@@Columbus## is a city", where special tokens @@## marks the entity to
extract. To efficiently address the "hallucination" issue of LLMs, where LLMs
have a strong inclination to over-confidently label NULL inputs as entities, we
propose a self-verification strategy by prompting LLMs to ask itself whether
the extracted entities belong to a labeled entity tag.
We conduct experiments on five widely adopted NER datasets, and GPT-NER
achieves comparable performances to fully supervised baselines, which is the
first time as far as we are concerned. More importantly, we find that GPT-NER
exhibits a greater ability in the low-resource and few-shot setups, when the
amount of training data is extremely scarce, GPT-NER performs significantly
better than supervised models. This demonstrates the capabilities of GPT-NER in
real-world NER applications where the number of labeled examples is limited
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
We present DiffBIR, which leverages pretrained text-to-image diffusion models
for blind image restoration problem. Our framework adopts a two-stage pipeline.
In the first stage, we pretrain a restoration module across diversified
degradations to improve generalization capability in real-world scenarios. The
second stage leverages the generative ability of latent diffusion models, to
achieve realistic image restoration. Specifically, we introduce an injective
modulation sub-network -- LAControlNet for finetuning, while the pre-trained
Stable Diffusion is to maintain its generative ability. Finally, we introduce a
controllable module that allows users to balance quality and fidelity by
introducing the latent image guidance in the denoising process during
inference. Extensive experiments have demonstrated its superiority over
state-of-the-art approaches for both blind image super-resolution and blind
face restoration tasks on synthetic and real-world datasets. The code is
available at https://github.com/XPixelGroup/DiffBIR
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