by jsendak | Jan 1, 2024 | Computer Science
Muon scattering tomography is a technique that utilizes muons, which are particles originating from cosmic rays, to create images of the interiors of dense objects. This technique has shown promise in various applications, including imaging volcanoes, detecting hidden chambers in archaeological sites, and monitoring nuclear waste repositories. However, existing reconstruction algorithms often suffer from low resolution and high noise due to the low flux of cosmic ray muons at sea-level and the complex interactions that muons undergo when they travel through matter.
In this groundbreaking research, a team has developed a novel two-stage deep learning algorithm called $mu$-Net to address the limitations of traditional reconstruction methods. The $mu$-Net algorithm consists of two components: an MLP (Multilayer Perceptron) that predicts the trajectory of the muon and a ConvNeXt-based U-Net that converts the scattering points into voxels.
The results of this study are impressive, with $mu$-Net achieving a state-of-the-art performance of 17.14 PSNR (Peak Signal-to-Noise Ratio) at the dosage of 1024 muons. This outperforms traditional reconstruction algorithms such as the point of closest approach algorithm and the maximum likelihood and expectation maximization algorithm. The high PSNR indicates improved image quality and reduced noise in the reconstructed images.
One of the key advantages of $mu$-Net is its robustness to various corruptions. This includes inaccuracies in the muon momentum or a limited detector resolution. This robustness is essential for real-world applications where uncertainties and imperfections are inevitable.
In addition to developing the $mu$-Net algorithm, the researchers have also generated and publicly released a large-scale dataset that maps muon detections to voxels. This dataset will be invaluable for further research and development in the field of muon scattering tomography.
This research opens up exciting possibilities for the future of muon scattering tomography. The application of deep learning algorithms has shown tremendous potential in improving image quality and resolution, which could lead to more accurate and detailed imaging of dense objects. Furthermore, the robustness of $mu$-Net to various corruptions paves the way for practical implementation in real-world scenarios.
Overall, this study highlights the immense progress that can be made by combining deep learning techniques with muon scattering tomography. It is expected that this research will inspire further investigations into the potential of deep learning to revolutionize this field and drive advancements in imaging technology.
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by jsendak | Jan 1, 2024 | Computer Science
Analysis:
The ESGReveal system is a significant advancement in the field of ESG data extraction and analysis. The use of Large Language Models (LLMs) enhanced with Retrieval Augmented Generation (RAG) techniques allows for more efficient and accurate retrieval of ESG information from corporate reports. This is a crucial development as the demand for reliable ESG data continues to grow, and stakeholders increasingly rely on this information to make informed decisions regarding corporate sustainability efforts.
The study’s evaluation of the ESGReveal system using ESG reports from 166 companies listed on the Hong Kong Stock Exchange in 2022 provides a comprehensive representation of the industry and market capitalization. The results of this evaluation demonstrate the efficacy of the system, with an accuracy of 76.9% in data extraction and 83.7% in disclosure analysis. These figures indicate an improvement over baseline models and highlight the system’s ability to refine ESG data analysis precision.
One noteworthy insight derived from the ESGReveal system is the demand for reinforced ESG disclosures. The study reveals that environmental and social data disclosures stood at 69.5% and 57.2%, respectively. These figures suggest that there is a pursuit for more corporate transparency, particularly in environmental and social aspects of sustainability. This finding emphasizes the importance of tools like ESGReveal in promoting accountability and driving corporate reporting practices towards greater transparency.
Looking ahead, the study acknowledges that current versions of ESGReveal do not process pictorial information, but it identifies this as a functionality to be included in future enhancements. Considering that visual elements often play a significant role in ESG reporting, the addition of pictorial information processing capabilities would further enhance the system’s analytical capabilities and enable a more comprehensive evaluation of corporate sustainability efforts.
The study also calls for continued research to further develop and compare the analytical capabilities of various Large Language Models (LLMs). As technology advances and new language models emerge, it will be important to assess their effectiveness and suitability for ESG data analysis. This ongoing research will contribute to the evolution of ESGReveal and help maintain its effectiveness in meeting the growing demand for reliable and comprehensive ESG information.
In summary, ESGReveal is a significant stride forward in ESG data processing. By providing stakeholders with a sophisticated tool for extracting and analyzing ESG information, it empowers them to better evaluate and advance corporate sustainability efforts. The system’s evolution holds promise for promoting transparency in corporate reporting, aligning with broader sustainable development aims, and driving positive change towards a more sustainable future.
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by jsendak | Jan 1, 2024 | Computer Science
Expert Commentary:
In this article, the authors propose a framework called TACIT for cross-domain text classification. Cross-domain text classification is the task of transferring models from label-rich source domains to label-poor target domains, which has various practical applications. The existing approaches in this field rely on unlabeled samples from the target domain, which limits their effectiveness when the target domain is agnostic. Additionally, these models are prone to shortcut learning in the source domain, which hampers their ability to generalize across domains.
TACIT addresses these challenges by introducing a target domain agnostic feature disentanglement framework using Variational Auto-Encoders (VAEs). VAEs are a type of generative model that can learn meaningful representations of the input data. In this framework, TACIT adaptively decouples robust and unrobust features, making the model more resistant to shortcut learning and improving its domain generalization ability.
To encourage the separation of unrobust features from robust ones, TACIT incorporates a feature distillation task. This task aims to compel unrobust features to approximate the output of a teacher model, which is trained using a few easy samples that may potentially have unknown shortcuts. This helps in effectively disentangling robust and unrobust features, enabling better cross-domain generalization.
The experimental results presented in the paper demonstrate that TACIT achieves comparable results to state-of-the-art baselines while utilizing only source domain data. This highlights the effectiveness of the proposed framework in overcoming the limitations of relying on target domain unlabeled samples and mitigating shortcut learning in the source domain.
Overall, TACIT presents a promising approach for cross-domain text classification by addressing the challenges of target domain agnosticism and shortcut learning. Future research could focus on extending this framework to other domains and exploring ways to further enhance the disentanglement of features for improved cross-domain generalization.
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by jsendak | Jan 1, 2024 | Computer Science
Revolutionizing Online Higher Education: Enabling Student Access to Specific Lecture Segments
The COVID-19 pandemic has brought about a profound shift in the way higher education is delivered, with remote teaching becoming the new norm. As universities adapt to this new online teaching-learning setting, the need for effective tools to support students’ learning experience has become increasingly apparent.
In response to this challenge, a team of researchers introduces a groundbreaking multimodal classification algorithm designed to identify various types of activities carried out during a lecture. By leveraging a transformer-based language model, this algorithm combines features from both the audio file and automated lecture transcription to determine the nature of the academic activity at any given time.
The impact of this algorithm cannot be overstated. Its main objective is to facilitate student access to specific segments of a lesson recording, allowing them to easily locate and review the teacher’s explanations of theoretical concepts, solution methods for exercises, or important organizational information related to the course.
The experimental results of this study reveal an interesting pattern: certain academic activities can be more accurately identified using the audio signal, while others require the text transcription for precise identification. This hybrid approach ensures comprehensive recognition of all academic activities undertaken by the teacher during a lesson.
This development marks a significant step forward in improving online learning experiences. By providing students with easy access to specific sections of lecture recordings, they can quickly review crucial information and reinforce their understanding of complex topics. It also promotes active engagement and self-directed learning, enabling students to actively choose areas they wish to revisit for better comprehension.
With the widespread adoption of remote teaching, this algorithm has the potential to revolutionize online higher education. By enhancing accessibility and easing navigation within lecture recordings, it ultimately empowers students in their educational journey.
The Future of Online Learning
The successful implementation of this algorithm raises exciting possibilities for the future of online learning. As educators continue to refine and advance the technology, we can expect further enhancements in supporting students’ learning experiences.
One potential avenue for development lies in expanding the algorithm’s capabilities to identify and categorize not only academic activities but also student interactions within the virtual classroom. By analyzing the audio and transcription data, it could track student engagement, participation, and even sentiment during different segments of the lesson. This valuable feedback can guide instructors in tailoring their teaching strategies and addressing individual student needs effectively.
Furthermore, as artificial intelligence continues to evolve, the algorithm could incorporate adaptive learning mechanisms. By leveraging machine learning algorithms, it could personalize the learning experience for individual students by identifying their strengths, weaknesses, and preferred learning styles. This individualized approach holds vast potential for optimizing learning outcomes and ensuring student success.
In conclusion, the introduction of this multimodal classification algorithm represents a crucial step forward in revolutionizing online higher education. By enabling students to easily access specific lecture segments, it empowers them to take control of their learning journey. As the technology advances further, we can anticipate even more exciting innovations that will reshape the landscape of online education.
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by jsendak | Jan 1, 2024 | Computer Science
Multi-modal multi-label emotion recognition (MMER) aims to identify relevant
emotions from multiple modalities. The challenge of MMER is how to effectively
capture discriminative features for multiple labels from heterogeneous data.
Recent studies are mainly devoted to exploring various fusion strategies to
integrate multi-modal information into a unified representation for all labels.
However, such a learning scheme not only overlooks the specificity of each
modality but also fails to capture individual discriminative features for
different labels. Moreover, dependencies of labels and modalities cannot be
effectively modeled. To address these issues, this paper presents ContrAstive
feature Reconstruction and AggregaTion (CARAT) for the MMER task. Specifically,
we devise a reconstruction-based fusion mechanism to better model fine-grained
modality-to-label dependencies by contrastively learning modal-separated and
label-specific features. To further exploit the modality complementarity, we
introduce a shuffle-based aggregation strategy to enrich co-occurrence
collaboration among labels. Experiments on two benchmark datasets CMU-MOSEI and
M3ED demonstrate the effectiveness of CARAT over state-of-the-art methods. Code
is available at https://github.com/chengzju/CARAT.
Multi-modal multi-label emotion recognition (MMER) is a challenging task that aims to identify relevant emotions from multiple modalities. This means that instead of relying on a single modality, such as text or audio, MMER incorporates multiple modalities, such as text, audio, and video, to capture a more comprehensive understanding of emotions.
The challenge in MMER lies in effectively capturing discriminative features for multiple labels from heterogeneous data. Heterogeneous data refers to different types of data, such as text, audio, and video, which all have their own unique characteristics. The goal is to find a way to combine these modalities in a way that effectively represents the emotions for each label.
Recent studies have focused on exploring fusion strategies to integrate multi-modal information into a unified representation for all labels. However, this approach overlooks the specificity of each modality and fails to capture individual discriminative features for different labels. It also doesn’t effectively model the dependencies between labels and modalities.
To address these issues, the authors of this paper propose ContrAstive feature Reconstruction and AggregaTion (CARAT), a new approach for MMER. CARAT uses a reconstruction-based fusion mechanism to better model fine-grained modality-to-label dependencies. By contrastively learning modal-separated and label-specific features, CARAT can capture the unique characteristics of each modality and label.
In addition, CARAT introduces a shuffle-based aggregation strategy to enrich co-occurrence collaboration among labels. This means that CARAT considers the relationships and interactions between different labels, allowing for a more comprehensive understanding of emotions.
The effectiveness of CARAT is demonstrated through experiments on two benchmark datasets, CMU-MOSEI and M3ED. The results show that CARAT outperforms state-of-the-art methods in multi-modal multi-label emotion recognition.
In the wider field of multimedia information systems, CARAT contributes to the study of how to effectively integrate multi-modal information for emotion recognition. By considering the specificities of each modality and capturing individual discriminative features, CARAT provides a more nuanced understanding of emotions.
Furthermore, CARAT is related to the fields of animations, artificial reality, augmented reality, and virtual realities. These fields often involve multi-modal data, as animations and virtual realities typically include visual and audio components. CARAT’s approach of combining different modalities can be applied to enhance the emotional realism and immersion in these multimedia experiences.
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by jsendak | Jan 1, 2024 | Computer Science
Expert Commentary
Multi-object tracking (MOT) is a challenging task in computer vision, where the goal is to estimate the trajectories of multiple objects over time. It has numerous applications in various fields, including surveillance, autonomous vehicles, and robotics. In this article, the authors address the problem of multi-object smoothing, where the object detections can be conditioned on all the measurements in a given time window.
Traditionally, Bayesian methods have been widely used for multi-object tracking and have achieved good results. However, the computational complexity of these methods increases exponentially with the number of objects being tracked, making them infeasible for large-scale scenarios.
To overcome this issue, the authors propose a deep learning (DL) based approach specifically designed for scenarios where accurate multi-object models are available and measurements are low-dimensional. Their proposed DL architecture separates the data association task from the smoothing task, which allows for more efficient and accurate tracking.
This is an exciting development as deep learning has shown great potential in various computer vision tasks. By leveraging deep neural networks, the proposed method is able to learn complex patterns from data and make more accurate predictions.
The authors evaluate their proposed approach against state-of-the-art Bayesian trackers and DL trackers in various tasks of varying difficulty. This comprehensive evaluation provides valuable insights into the performance of different methods in the multi-object tracking smoothing problem setting.
Overall, this research introduces a novel DL architecture tailored for accurate multi-object tracking, addressing the limitations of existing Bayesian trackers. It opens up possibilities for improved performance and scalability in complex multi-object tracking scenarios. Further research could focus on refining the proposed DL architecture and conducting experiments on more diverse datasets to assess its generalizability.
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