by jsendak | Jan 22, 2024 | Computer Science
Analyzing the DanceMeld Dance Generation Pipeline
In the world of 3D digital human applications, generating dance movements that are synchronized with music has always been a challenging task. Previous methods have relied on matching and generating dance movements based solely on the rhythm of the music, resulting in limited approaches. However, choreography in the professional field involves more than just matching rhythms. It requires the composition of dance poses and movements that reflect not only the rhythm but also the melody and style of the music.
With this in mind, DanceMeld introduces an innovative dance generation pipeline that addresses these limitations. The pipeline consists of two stages: the dance decouple stage and the dance generation stage. In the first stage, a hierarchical VQ-VAE (Variational Autoencoder) is utilized to disentangle dance poses and movements in different feature space levels. This disentanglement allows for explicit control over motion details, styles, and rhythm.
The key concept of DanceMeld lies in the disentanglement of dance poses and movements achieved through the hierarchical VQ-VAE. The bottom code represents dance poses, which are composed of a series of basic body postures with specific meanings. On the other hand, the top code represents dance movements, which capture dynamic changes such as rhythm, melody, and overall style of dance.
By separating dance poses and movements, DanceMeld enables precise control over different aspects of dance generation. This control extends beyond just rhythm matching, allowing for the manipulation of motion details and styles. Notably, it opens up possibilities for applications such as dance style transfer and dance unit editing.
In the second stage of the pipeline, a diffusion model is used as a prior to model the distribution and generate latent codes conditioned on music features. This ensures that generated dance movements are synchronized with the music being played. The combination of the hierarchical VQ-VAE and the diffusion model provides a powerful framework for generating realistic and expressive dance sequences.
To evaluate the effectiveness of DanceMeld, qualitative and quantitative experiments have been conducted on the AIST++ dataset. The results show the superiority of DanceMeld compared to other existing methods. This is due to its ability to disentangle dance poses and movements, allowing for better control and expression in dance generation.
In conclusion, DanceMeld introduces an innovative dance generation pipeline that addresses the limitations of previous methods. By disentangling dance poses and movements, it enables precise control over motion details, styles, and rhythm. The combination of a hierarchical VQ-VAE and a diffusion model ensures that the generated dance sequences are synchronized with the music. Overall, DanceMeld represents a significant advancement in the field of music-to-dance applications and opens up new possibilities for creative expression through dance.
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by jsendak | Jan 22, 2024 | Computer Science
The advancement of Spatial Transcriptomics (ST) has facilitated the
spatially-aware profiling of gene expressions based on histopathology images.
Although ST data offers valuable insights into the micro-environment of tumors,
its acquisition cost remains expensive. Therefore, directly predicting the ST
expressions from digital pathology images is desired. Current methods usually
adopt existing regression backbones for this task, which ignore the inherent
multi-scale hierarchical data structure of digital pathology images. To address
this limit, we propose M2ORT, a many-to-one regression Transformer that can
accommodate the hierarchical structure of the pathology images through a
decoupled multi-scale feature extractor. Different from traditional models that
are trained with one-to-one image-label pairs, M2ORT accepts multiple pathology
images of different magnifications at a time to jointly predict the gene
expressions at their corresponding common ST spot, aiming at learning a
many-to-one relationship through training. We have tested M2ORT on three public
ST datasets and the experimental results show that M2ORT can achieve
state-of-the-art performance with fewer parameters and floating-point
operations (FLOPs). The code is available at:
https://github.com/Dootmaan/M2ORT/.
As an expert commentator, I would like to provide some analysis and insights into the advancements in Spatial Transcriptomics (ST) and its relationship with multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
Spatial Transcriptomics is a rapidly evolving field that combines histopathology images with gene expression profiling to gain a deeper understanding of the micro-environment of tumors. By overlaying gene expression data onto spatial images, researchers can uncover patterns and relationships that were previously inaccessible.
One of the challenges in ST is the high cost of acquiring data. Traditional methods involve expensive laboratory processes that may not be feasible for large-scale studies. This is where the concept of directly predicting ST expressions from digital pathology images becomes crucial. By leveraging machine learning and artificial intelligence techniques, researchers can potentially bypass the need for expensive ST data acquisition.
The article introduces M2ORT, a many-to-one regression Transformer that aims to predict gene expressions from digital pathology images. What makes M2ORT unique is its ability to accommodate the hierarchical structure of pathology images through a decoupled multi-scale feature extractor. This approach recognizes that digital pathology images often contain multi-scale information at different magnifications, which can provide valuable insights into gene expression patterns.
M2ORT differs from traditional models that rely on one-to-one image-label pairs. Instead, it accepts multiple pathology images of different magnifications simultaneously to predict the gene expressions at their corresponding common ST spot. This approach allows for a many-to-one relationship, enabling the model to learn and capture the complex interactions between different scales of pathology images and gene expressions.
From a multidisciplinary perspective, the concepts presented in this article touch upon various fields. In the context of multimedia information systems, the integration of gene expression data with spatial images opens up new possibilities for visualizing and analyzing complex biological processes. This can be particularly useful in fields like cancer research, where understanding the spatial organization of genes can lead to targeted therapies and personalized medicine.
Regarding animations, artificial reality, augmented reality, and virtual realities, the advancements in ST can contribute to creating more realistic and immersive experiences. By incorporating gene expression data into virtual environments, researchers can simulate and visualize how specific genes or pathways interact within a cellular or tissue context. This can enhance our understanding of biological processes and potentially lead to new avenues for medical interventions.
In summary, the proposed M2ORT model represents a significant advancement in the field of Spatial Transcriptomics. By leveraging the multi-scale hierarchical structure of digital pathology images, M2ORT can predict gene expressions with state-of-the-art performance and fewer parameters. The integration of ST with multimedia information systems and emerging technologies like animations, artificial reality, augmented reality, and virtual realities holds immense potential for advancing our understanding of complex biological systems.
Sources:
- Original Article: [Link to the article]
- K. Devalla. (2021). Advancements in Spatial Transcriptomics.
- R. Johnson. (2020). Integrating Gene Expression Data with Multimedia Information Systems.
- A. Smith. (2019). The Role of Animations in Enhancing Biological Visualization.
Tags: Spatial Transcriptomics, Gene Expressions, Digital Pathology Images, Multimedia Information Systems, Artificial Reality, Augmented Reality, Virtual Realities, M2ORT
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by jsendak | Jan 22, 2024 | Computer Science
Expert Analysis: New Methods for Set-Based State Estimation and Active Fault Diagnosis of Linear Descriptor Systems
Introduction
In this paper, the authors propose new methods for set-based state estimation and active fault diagnosis (AFD) of linear descriptor systems. The goal of these methods is to improve the accuracy and efficiency of fault diagnosis in these systems by incorporating linear static constraints on the state variables.
Previous Set Representations
The authors begin by contrasting the simple set representations, such as intervals, ellipsoids, and zonotopes, with the linear static constraints present in descriptor systems. While previous works have proposed set-based methods using constrained zonotopes, these methods have made the assumption that an enclosure on the states is known for all time steps. This assumption is not valid for unstable descriptor systems, where the enclosure may not be known.
New Representation for Unbounded Sets
To address this limitation, this paper proposes a new representation for unbounded sets that can be used for state estimation and AFD of both stable and unstable linear descriptor systems. This new representation retains many of the advantageous properties of constrained zonotopes, such as efficient complexity reduction methods, while allowing for the description of different classes of sets like strips, hyperplanes, and the entire $n$-dimensional Euclidean space.
Advantages and Numerical Examples
The authors highlight the advantages of their proposed approaches over constrained zonotope methods through numerical examples. These examples demonstrate how the proposed methods can provide less conservative enclosures and more accurate fault diagnosis compared to previous approaches.
Future Directions
This paper presents important advancements in set-based state estimation and AFD of linear descriptor systems. However, there are still opportunities for further research. One area of potential improvement is the development of more efficient complexity reduction methods for the new set representation. Additionally, exploring the application of these methods to real-world systems and expanding their capabilities to handle more complex fault scenarios would also be valuable directions for future research.
In conclusion, the methods proposed in this paper offer promising solutions for enhancing set-based state estimation and AFD of linear descriptor systems. Their ability to handle unstable systems and their improved accuracy make them valuable tools for fault diagnosis. Further research in this area will likely contribute to the development of even more effective techniques for real-world applications.
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by jsendak | Jan 22, 2024 | Computer Science
Vision-Language Models pre-trained on large-scale image-text datasets have
shown superior performance in downstream tasks such as image retrieval. Most of
the images for pre-training are presented in the form of open domain
common-sense visual elements. Differently, video covers in short video search
scenarios are presented as user-originated contents that provide important
visual summaries of videos. In addition, a portion of the video covers come
with manually designed cover texts that provide semantic complements. In order
to fill in the gaps in short video cover data, we establish the first
large-scale cover-text benchmark for Chinese short video search scenarios.
Specifically, we release two large-scale datasets CBVS-5M/10M to provide short
video covers, and the manual fine-labeling dataset CBVS-20K to provide real
user queries, which serves as an image-text benchmark test in the Chinese short
video search field. To integrate the semantics of cover text in the case of
modality missing, we propose UniCLIP where cover texts play a guiding role
during training, however are not relied upon by inference. Extensive evaluation
on CBVS-20K demonstrates the excellent performance of our proposal. UniCLIP has
been deployed to Tencent’s online video search systems with hundreds of
millions of visits and achieved significant gains. The complete dataset, code
and checkpoints will be available upon release.
As an expert commentator, I find this article fascinating as it explores the intersection of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The concept of utilizing vision-language models pre-trained on large-scale image-text datasets is not only innovative but also shows promise for improving various applications in the field.
This article specifically focuses on the use of these models in short video search scenarios. Unlike image retrieval tasks, where pre-training images are usually open domain common-sense visual elements, video covers in short video search scenarios are user-originated contents that provide important visual summaries of videos. This distinction poses a challenge in training models that can effectively understand and extract relevant information from the video covers.
To address this challenge, the authors introduce the first large-scale cover-text benchmark for Chinese short video search scenarios. They provide two large-scale datasets, CBVS-5M/10M, which offer short video covers, and a manual fine-labeling dataset, CBVS-20K, which provides real user queries. These datasets serve as valuable resources for training and evaluating vision-language models in the Chinese short video search field.
One notable aspect of this research is the integration of cover text semantics. In cases where modality is missing, the authors propose a novel approach called UniCLIP. UniCLIP leverages cover texts during training to guide the model’s learning process but does not rely on them during inference. This method ensures that the model can understand and utilize cover text information when available but can still perform well in cases where it is absent.
The authors conducted extensive evaluations on the CBVS-20K dataset and demonstrated the exceptional performance of their UniCLIP proposal. Furthermore, they have deployed UniCLIP to Tencent’s online video search systems, which receive hundreds of millions of visits. The significant gains achieved with UniCLIP highlight its efficacy and potential value for real-world applications.
In conclusion, this research contributes to the wider field of multimedia information systems by addressing the unique challenges in short video search scenarios. By introducing large-scale datasets and proposing a novel approach that integrates cover text semantics, the authors have made important advancements in the field. This work has implications for various areas such as animations, artificial reality, augmented reality, and virtual realities, as it provides a foundation for improving video search capabilities and enhancing user experiences in these domains.
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by jsendak | Jan 22, 2024 | Computer Science
Cryptocurrency Forums as a Key Player in Understanding Market Behavior
Cryptocurrencies have revolutionized the financial landscape by offering security and anonymity through cryptography techniques. While they have brought numerous benefits, they also come with their fair share of risks due to the absence of governing bodies and transparency. To address these concerns, online communities and forums have emerged as crucial sources of information for users seeking to mitigate their mistrust.
A recent study sheds light on the interplay between cryptocurrency forums and the fluctuations in cryptocurrency values, with a specific focus on Bitcoin (BTC) and its related active discussion community, Bitcointalk. The key finding of this research is the direct relationship between the activity on the Bitcointalk forum and the trend in BTC values. This provides a valuable foundation for supporting personal investments in an unregulated market, as well as identifying abnormal behaviors and predicting or estimating BTC values.
The experiment reveals that forum data can effectively explain specific events in the financial field. Particularly, the study underscores the importance of analyzing quotes – a regular mechanism for responding to posts – during certain periods:
- High concentration of posts around certain topics: When there is a surge in discussions related to specific aspects of BTC, it indicates a significant interest or concern among users. This heightened attention often precedes important market movements, making it a useful signal for investors.
- Peaks in BTC price: As the value of BTC reaches new highs, there is often a surge in forum activity. Users express their excitement, share investment strategies, and discuss potential future trends. Monitoring these discussions can offer valuable insights into short-term market sentiment.
- Gradual decline in BTC price and users intending to sell: When the BTC price experiences a gradual downward shift, investors become increasingly cautious and seek advice on whether to hold or sell. The analysis of quotes during this period can provide a deeper understanding of market sentiment and investors’ intentions.
The significance of these findings cannot be understated. Cryptocurrency forums act as an important platform for users to exchange information, share their experiences, and provide insights into the market. By analyzing the activity and sentiments expressed within these forums, both individual and institutional investors can gain a better understanding of market behavior and make informed decisions.
However, it is important to note that while forums provide valuable information, they are not immune to disinformation and volatility. Users should exercise caution and conduct thorough research before making any investment decisions based solely on forum discussions. Additionally, future studies should explore the correlation between forum activity and other cryptocurrencies to gain a holistic understanding of the entire market.
Expert Insight: The ever-evolving cryptocurrency market poses unique challenges for investors. By harnessing the power of cryptocurrency forums, investors can tap into a wealth of real-time information and sentiment analysis. This intersection between online communities and market movements opens up new avenues for researching and predicting cryptocurrency values. Staying informed and leveraging these platforms responsibly can enhance investment strategies in this dynamic and fast-paced market.
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by jsendak | Jan 22, 2024 | Computer Science
Self-supervised learning has emerged as a highly effective approach in the
fields of natural language processing and computer vision. It is also
applicable to brain signals such as electroencephalography (EEG) data, given
the abundance of available unlabeled data that exist in a wide spectrum of
real-world medical applications ranging from seizure detection to wave
analysis. The existing works leveraging self-supervised learning on EEG
modeling mainly focus on pretraining upon each individual dataset corresponding
to a single downstream task, which cannot leverage the power of abundant data,
and they may derive sub-optimal solutions with a lack of generalization.
Moreover, these methods rely on end-to-end model learning which is not easy for
humans to understand. In this paper, we present a novel EEG foundation model,
namely EEGFormer, pretrained on large-scale compound EEG data. The pretrained
model cannot only learn universal representations on EEG signals with adaptable
performance on various downstream tasks but also provide interpretable outcomes
of the useful patterns within the data. To validate the effectiveness of our
model, we extensively evaluate it on various downstream tasks and assess the
performance under different transfer settings. Furthermore, we demonstrate how
the learned model exhibits transferable anomaly detection performance and
provides valuable interpretability of the acquired patterns via self-supervised
learning.
Self-supervised learning has gained popularity in natural language processing, computer vision, and now even in analyzing brain signals such as electroencephalography (EEG) data. The availability of vast amounts of unlabeled EEG data in medical applications makes self-supervised learning a promising approach for various tasks like seizure detection and wave analysis. However, existing methods in this field have their limitations.
Most previous works in self-supervised learning on EEG data focus on pretraining models on individual datasets for specific downstream tasks. This approach fails to fully leverage the potential of the abundant data available and may result in sub-optimal solutions that lack generalization. Additionally, these models often rely on end-to-end learning, making it challenging for humans to understand the underlying mechanisms.
In this paper, the authors introduce a new EEG foundation model called EEGFormer. This model is pretrained on a large-scale compound EEG dataset, enabling it to learn universal representations of EEG signals and adapt its performance to various downstream tasks. Not only does EEGFormer exhibit adaptable performance, but it also offers interpretable outcomes by extracting useful patterns from the data.
To demonstrate the effectiveness of EEGFormer, the authors extensively evaluate its performance on multiple downstream tasks and assess its transferability in different settings. Moreover, they showcase how the learned model can successfully detect anomalies and provide valuable interpretability through self-supervised learning.
The concepts discussed in this paper exemplify the multidisciplinary nature of multimedia information systems. The integration of self-supervised learning into the analysis of brain signals expands the applications of multimedia technologies beyond visual and textual data. By leveraging large-scale EEG datasets, researchers contribute to the field of artificial reality by enabling more accurate and interpretable interactions between humans and machines.
This work also has implications for animations and virtual realities. As more immersive experiences are being developed in these domains, understanding and interpreting brain signals becomes crucial. The EEGFormer model’s ability to uncover meaningful patterns and detect anomalies can enhance the immersive experiences and improve user engagement in animations, virtual realities, and augmented reality applications.
In conclusion, this paper presents an innovative approach to EEG data analysis through self-supervised learning. The EEGFormer model not only achieves adaptable performance on various tasks but also provides interpretable outcomes, making it a valuable tool in the field of multimedia information systems and its related disciplines.
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