82 research outputs found
K-HATERS: A Hate Speech Detection Corpus in Korean with Target-Specific Ratings
Numerous datasets have been proposed to combat the spread of online hate.
Despite these efforts, a majority of these resources are English-centric,
primarily focusing on overt forms of hate. This research gap calls for
developing high-quality corpora in diverse languages that also encapsulate more
subtle hate expressions. This study introduces K-HATERS, a new corpus for hate
speech detection in Korean, comprising approximately 192K news comments with
target-specific offensiveness ratings. This resource is the largest offensive
language corpus in Korean and is the first to offer target-specific ratings on
a three-point Likert scale, enabling the detection of hate expressions in
Korean across varying degrees of offensiveness. We conduct experiments showing
the effectiveness of the proposed corpus, including a comparison with existing
datasets. Additionally, to address potential noise and bias in human
annotations, we explore a novel idea of adopting the Cognitive Reflection Test,
which is widely used in social science for assessing an individual's cognitive
ability, as a proxy of labeling quality. Findings indicate that annotations
from individuals with the lowest test scores tend to yield detection models
that make biased predictions toward specific target groups and are less
accurate. This study contributes to the NLP research on hate speech detection
and resource construction. The code and dataset can be accessed at
https://github.com/ssu-humane/K-HATERS.Comment: 15 pages, EMNLP 2023 (Findings
RADIO: Reference-Agnostic Dubbing Video Synthesis
One of the most challenging problems in audio-driven talking head generation
is achieving high-fidelity detail while ensuring precise synchronization. Given
only a single reference image, extracting meaningful identity attributes
becomes even more challenging, often causing the network to mirror the facial
and lip structures too closely. To address these issues, we introduce RADIO, a
framework engineered to yield high-quality dubbed videos regardless of the pose
or expression in reference images. The key is to modulate the decoder layers
using latent space composed of audio and reference features. Additionally, we
incorporate ViT blocks into the decoder to emphasize high-fidelity details,
especially in the lip region. Our experimental results demonstrate that RADIO
displays high synchronization without the loss of fidelity. Especially in harsh
scenarios where the reference frame deviates significantly from the ground
truth, our method outperforms state-of-the-art methods, highlighting its
robustness. Pre-trained model and codes will be made public after the review.Comment: Under revie
Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation
Diffusion generative modeling has become a promising approach for learning
robotic manipulation tasks from stochastic human demonstrations. In this paper,
we present Diffusion-EDFs, a novel SE(3)-equivariant diffusion-based approach
for visual robotic manipulation tasks. We show that our proposed method
achieves remarkable data efficiency, requiring only 5 to 10 human
demonstrations for effective end-to-end training in less than an hour.
Furthermore, our benchmark experiments demonstrate that our approach has
superior generalizability and robustness compared to state-of-the-art methods.
Lastly, we validate our methods with real hardware experiments. Project
Website: https://sites.google.com/view/diffusion-edfs/homeComment: 31 pages, 13 figure
Pixel-Level Equalized Matching for Video Object Segmentation
Feature similarity matching, which transfers the information of the reference
frame to the query frame, is a key component in semi-supervised video object
segmentation. If surjective matching is adopted, background distractors can
easily occur and degrade the performance. Bijective matching mechanisms try to
prevent this by restricting the amount of information being transferred to the
query frame, but have two limitations: 1) surjective matching cannot be fully
leveraged as it is transformed to bijective matching at test time; and 2)
test-time manual tuning is required for searching the optimal hyper-parameters.
To overcome these limitations while ensuring reliable information transfer, we
introduce an equalized matching mechanism. To prevent the reference frame
information from being overly referenced, the potential contribution to the
query frame is equalized by simply applying a softmax operation along with the
query. On public benchmark datasets, our proposed approach achieves a
comparable performance to state-of-the-art methods
Targeting Liver X Receptors for the Treatment of Non-Alcoholic Fatty Liver Disease
Non-alcoholic fatty liver disease (NAFLD) refers to a range of conditions in which excess lipids accumulate in the liver, possibly leading to serious hepatic manifestations such as steatohepatitis, fibrosis/cirrhosis and cancer. Despite its increasing prevalence and significant impact on liver disease-associated mortality worldwide, no medication has been approved for the treatment of NAFLD yet. Liver X receptors Ī±/Ī² (LXRĪ± and LXRĪ²) are lipid-activated nuclear receptors that serve as master regulators of lipid homeostasis and play pivotal roles in controlling various metabolic processes, including lipid metabolism, inflammation and immune response. Of note, NAFLD progression is characterized by increased accumulation of triglycerides and cholesterol, hepatic de novo lipogenesis, mitochondrial dysfunction and augmented inflammation, all of which are highly attributed to dysregulated LXR signaling. Thus, targeting LXRs may provide promising strategies for the treatment of NAFLD. However, emerging evidence has revealed that modulating the activity of LXRs has various metabolic consequences, as the main functions of LXRs can distinctively vary in a cell type-dependent manner. Therefore, understanding how LXRs in the liver integrate various signaling pathways and regulate metabolic homeostasis from a cellular perspective using recent advances in research may provide new insights into therapeutic strategies for NAFLD and associated metabolic diseases
The case report of an Anaphylaxis occurred when using Sweet Bee Venom and common Bee Venom at the same time
Objectives: The purpose of this case report are to introduce an Anaphylaxis occurred when using Sweet Bee Venom (SBV) and common Bee Venom (CBC) at the same time and a risk when used SBV and CBC together without skin test.
Methods: A patient, an Anaphylaxis occurred when treated her with SBV and CBC at the same time without additional skin test, we observed the progress of the Anaphylaxis with care for her.
Results & Conclusions: The Anaphylaxis in the patient was taken a turn for the better by emergency response. Based on this case, Sometimes the use of SBV and CBC at the same time without skin test can be dangerous, and to avoid the risk when used CBC together, in advance allergy test should be conducted
Direct Use of a Saliva-Collected Cotton Swab in Lateral Flow Immunoassay for the Detection of Cotinine
The detection of salivary cotinine is useful for convenient smoking tests in spite of the high background effect of saliva. For precise results, the conventional salivary cotinine analysis for smoking detection requires complex pretreatment processes. Hence, in this study, we developed a modified paper-based lateral flow immunoassay (LFIA), termed āgap-LFIAā, for the direct application of saliva collected using cotton swabs for on-site detection. The gap-LFIA was constructed by modifying a conventional LFIA sensor, where the sample pad was divided to have a 3 mm gap. A saliva-collected cotton swab was inserted into the gap, and then, a buffer solution was added to the outer sample pad to dilute the saliva automatically. The gap-LFIA reduced the interference in salivary samples and showed improved signals, allowing for using the whole saliva directly without additional steps. Further, the deviation of results using a strip was less than that when the saliva was not diluted in a conventional cotinine kit, and it helped to distinguish between smokers and non-smokers more clearly in 15 min. This method of automatic dilution may apply to various clinical samples, including blood and serum, for direct application in future detections
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