40 research outputs found
Colourings of -coloured mixed graphs
A mixed graph is, informally, an object obtained from a simple undirected
graph by choosing an orientation for a subset of its edges. A mixed graph is
-coloured if each edge is assigned one of colours, and each
arc is assigned one of colours. Oriented graphs are -coloured mixed graphs, and 2-edge-coloured graphs are -coloured
mixed graphs. We show that results of Sopena for vertex colourings of oriented
graphs, and of Kostochka, Sopena and Zhu for vertex colourings oriented graphs
and 2-edge-coloured graphs, are special cases of results about vertex
colourings of -coloured mixed graphs. Both of these can be regarded as
a version of Brooks' Theorem.Comment: 7 pages, no figure
Using Panoramic Videos for Multi-person Localization and Tracking in a 3D Panoramic Coordinate
3D panoramic multi-person localization and tracking are prominent in many
applications, however, conventional methods using LiDAR equipment could be
economically expensive and also computationally inefficient due to the
processing of point cloud data. In this work, we propose an effective and
efficient approach at a low cost. First, we obtain panoramic videos with four
normal cameras. Then, we transform human locations from a 2D panoramic image
coordinate to a 3D panoramic camera coordinate using camera geometry and human
bio-metric property (i.e., height). Finally, we generate 3D tracklets by
associating human appearance and 3D trajectory. We verify the effectiveness of
our method on three datasets including a new one built by us, in terms of 3D
single-view multi-person localization, 3D single-view multi-person tracking,
and 3D panoramic multi-person localization and tracking. Our code and dataset
are available at \url{https://github.com/fandulu/MPLT}.Comment: 5 page
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
Graph collaborative filtering, which learns user and item representations
through message propagation over the user-item interaction graph, has been
shown to effectively enhance recommendation performance. However, most current
graph collaborative filtering models mainly construct the interaction graph on
a single behavior domain (e.g. click), even though users exhibit various types
of behaviors on real-world platforms, including actions like click, cart, and
purchase. Furthermore, due to variations in user engagement, there exists an
imbalance in the scale of different types of behaviors. For instance, users may
click and view multiple items but only make selective purchases from a small
subset of them. How to alleviate the behavior imbalance problem and utilize
information from the multiple behavior graphs concurrently to improve the
target behavior conversion (e.g. purchase) remains underexplored. To this end,
we propose IMGCF, a simple but effective model to alleviate behavior data
imbalance for multi-behavior graph collaborative filtering. Specifically, IMGCF
utilizes a multi-task learning framework for collaborative filtering on
multi-behavior graphs. Then, to mitigate the data imbalance issue, IMGCF
improves representation learning on the sparse behavior by leveraging
representations learned from the behavior domain with abundant data volumes.
Experiments on two widely-used multi-behavior datasets demonstrate the
effectiveness of IMGCF.Comment: accepted by ICDM2023 Worksho
META-SELD: Meta-Learning for Fast Adaptation to the new environment in Sound Event Localization and Detection
For learning-based sound event localization and detection (SELD) methods,
different acoustic environments in the training and test sets may result in
large performance differences in the validation and evaluation stages.
Different environments, such as different sizes of rooms, different
reverberation times, and different background noise, may be reasons for a
learning-based system to fail. On the other hand, acquiring annotated spatial
sound event samples, which include onset and offset time stamps, class types of
sound events, and direction-of-arrival (DOA) of sound sources is very
expensive. In addition, deploying a SELD system in a new environment often
poses challenges due to time-consuming training and fine-tuning processes. To
address these issues, we propose Meta-SELD, which applies meta-learning methods
to achieve fast adaptation to new environments. More specifically, based on
Model Agnostic Meta-Learning (MAML), the proposed Meta-SELD aims to find good
meta-initialized parameters to adapt to new environments with only a small
number of samples and parameter updating iterations. We can then quickly adapt
the meta-trained SELD model to unseen environments. Our experiments compare
fine-tuning methods from pre-trained SELD models with our Meta-SELD on the
Sony-TAU Realistic Spatial Soundscapes 2023 (STARSSS23) dataset. The evaluation
results demonstrate the effectiveness of Meta-SELD when adapting to new
environments.Comment: Submitted to DCASE 2023 Worksho
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis leverages multimodal signals to detect the
sentiment of a speaker. Previous approaches concentrate on performing
multimodal fusion and representation learning based on general knowledge
obtained from pretrained models, which neglects the effect of domain-specific
knowledge. In this paper, we propose Contrastive Knowledge Injection (ConKI)
for multimodal sentiment analysis, where specific-knowledge representations for
each modality can be learned together with general knowledge representations
via knowledge injection based on an adapter architecture. In addition, ConKI
uses a hierarchical contrastive learning procedure performed between knowledge
types within every single modality, across modalities within each sample, and
across samples to facilitate the effective learning of the proposed
representations, hence improving multimodal sentiment predictions. The
experiments on three popular multimodal sentiment analysis benchmarks show that
ConKI outperforms all prior methods on a variety of performance metrics.Comment: Accepted by ACL Findings 202
Lipid-lowering drugs affect lung cancer risk via sphingolipid metabolism: a drug-target Mendelian randomization study
Background: The causal relationship between lipid-lowering drug (LLD) use and lung cancer risk is controversial, and the role of sphingolipid metabolism in this effect remains unclear.Methods: Genome-wide association study data on low-density lipoprotein (LDL), apolipoprotein B (ApoB), and triglycerides (TG) were used to develop genetic instrumental variables (IVs) for LLDs. Two-step Mendelian randomization analyses were performed to examine the causal relationship between LLDs and lung cancer risk. The effects of ceramide, sphingosine-1-phosphate (S1P), and ceramidases on lung cancer risk were explored, and the proportions of the effects of LLDs on lung cancer risk mediated by sphingolipid metabolism were calculated.Results:APOB inhibition decreased the lung cancer risk in ever-smokers via ApoB (odds ratio [OR] 0.81, 95% confidence interval [CI] 0.70–0.92, p = 0.010), LDL (OR 0.82, 95% CI 0.71–0.96, p = 0.040), and TG (OR 0.63, 95% CI 0.46–0.83, p = 0.015) reduction by 1 standard deviation (SD), decreased small-cell lung cancer (SCLC) risk via LDL reduction by 1 SD (OR 0.71, 95% CI 0.56–0.90, p = 0.016), and decreased the plasma ceramide level and increased the neutral ceramidase level. APOC3 inhibition decreased the lung adenocarcinoma (LUAD) risk (OR 0.60, 95% CI 0.43–0.84, p = 0.039) but increased SCLC risk (OR 2.18, 95% CI 1.17–4.09, p = 0.029) via ApoB reduction by 1 SD. HMGCR inhibition increased SCLC risk via ApoB reduction by 1 SD (OR 3.04, 95% CI 1.38–6.70, p = 0.014). The LPL agonist decreased SCLC risk via ApoB (OR 0.20, 95% CI 0.07–0.58, p = 0.012) and TG reduction (OR 0.58, 95% CI 0.43–0.77, p = 0.003) while increased the plasma S1P level. PCSK9 inhibition decreased the ceramide level. Neutral ceramidase mediated 8.1% and 9.5% of the reduced lung cancer risk in ever-smokers via ApoB and TG reduction by APOB inhibition, respectively, and mediated 8.7% of the reduced LUAD risk via ApoB reduction by APOC3 inhibition.Conclusion: We elucidated the intricate interplay between LLDs, sphingolipid metabolites, and lung cancer risk. Associations of APOB, APOC3, and HMGCR inhibition and LPL agonist with distinct lung cancer risks underscore the multifaceted nature of these relationships. The observed mediation effects highlight the considerable influence of neutral ceramidase on the lung cancer risk reduction achieved by APOB and APOC3 inhibition
Comparison of Different Methods on EEG Signal Separating of Stuttering Adult and Child During the Pre-speech Auditory Modulation
Thesis (Master's)--University of Washington, 2020During the Event-related potential (ERP) study, ideally, the EEG recording only contains the event-related signal. However, there could exist irrelevant signals and noise. Unconscious activities, such as eye movement and muscle movement, and activities caused by the design of the experiment, could occur during the recording sessions. Meanwhile, due to the hyperactive nature of the child, there is more irrelevant signal inside child EEG signals. To solve this problem. there are three methods discussed in this paper, which are averaging, independent component analysis (ICA), and Autoencoder. Averaging is the classical method applying to process data in ERP studies. Two advantages of this method are: 1) preserving the original information of the data most 2) eliminating non-activity-related Gaussian noise. There also are two pitfalls: 1) reducing the number of epoch in each group 2) failing to remove the irrelevant activity-related signals. This method is also unable to get useful information from the child data. And the signal to noise ratio (SNR) of this method is 30.21 for adult subjects. ICA, a linear blind source separation method, is also a common method used by some of the studies. There are two advantages to this method: 1) preserving the number of epoch in each group. 2) removing the irrelevant eye movement and muscle movement signals. One pitfall is that bad rejection choice may cause losing information. This method improves some of the results in child subjects. And the SNR of this method is 33.02 for adult subjects, which is higher than averaging. Autoencoder is a nonlinear dimensionality reduction method. By creating proper loss function, a nonlinear independent feature learning method is applied to the EEG signals. The advantages are 1) nonlinearly learning the feature and linearly reconstructing the data at the same time 2) dimensionality reduction. One pitfall is currently no localization method to validate the features. And the SNR of this method is 22.94 for adult subjects, which is lower than averaging. And Autoencoder also can process part of the child data