by jsendak | Jan 15, 2024 | Computer Science
Amidst ongoing market recalibration and increasing investor optimism, the U.S. stock market is experiencing a resurgence, prompting the need for sophisticated tools to protect and grow portfolios. Addressing this, we introduce “Stockformer,” a cutting-edge deep learning framework optimized for swing trading, featuring the TopKDropout method for enhanced stock selection.
Deep learning has gained significant attention in recent years due to its ability to analyze complex patterns and make accurate predictions. Stockformer takes advantage of deep learning techniques to analyze the intricate data of the S&P 500, refining stock return predictions and providing investors with valuable insights.
The use of STL decomposition, a widely-used time series analysis technique, in Stockformer allows for the decomposition of the underlying trend, seasonality, and irregular components in the data. This decomposition enables the model to better understand the patterns and characteristics of the stock market, leading to more accurate predictions.
In addition, Stockformer leverages self-attention networks, a powerful mechanism in natural language processing and image recognition, to capture long-range dependencies within the data. By considering the relationships between different time steps, the model can make informed predictions that incorporate historical information and market trends.
To evaluate the performance of Stockformer, the study conducted tests on a dataset spanning from January 2021 to January 2023 for training and validation purposes. The testing phase took place from February to June 2023, where Stockformer’s predictions were compared against ten industry models.
The results were impressive, with Stockformer outperforming all other models in key predictive accuracy indicators such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Additionally, Stockformer exhibited a remarkable accuracy rate of 62.39% in detecting market trends, providing valuable guidance for investors looking to capitalize on favorable market conditions.
The backtests conducted with Stockformer’s swing trading strategy showed promising results, with a cumulative return of 13.19% and an annualized return of 30.80%. These returns significantly surpassed the performance of current state-of-the-art models, highlighting the effectiveness and reliability of Stockformer in generating profitable trading strategies.
Stockformer is a beacon of innovation in these volatile times, offering investors a potent tool for market forecasting. By open-sourcing the framework, the creators of Stockformer aim to foster community collaboration, allowing other researchers and traders to build upon the work and contribute to advancing the field further.
Overall, Stockformer introduces a state-of-the-art deep learning framework that successfully tackles the challenges of swing trading in the U.S. stock market. Its incorporation of STL decomposition, self-attention networks, and the TopKDropout method showcases the sophistication and optimization employed to provide investors with accurate predictions and profitable trading strategies. As the market continues to evolve, it will be interesting to see how Stockformer evolves alongside it, potentially incorporating additional features and refining its predictions.
To learn more about Stockformer and access the open-source code, visit https://example.com.
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by jsendak | Jan 15, 2024 | Computer Science
The audio spectrogram is a time-frequency representation that has been widely
used for audio classification. One of the key attributes of the audio
spectrogram is the temporal resolution, which depends on the hop size used in
the Short-Time Fourier Transform (STFT). Previous works generally assume the
hop size should be a constant value (e.g., 10 ms). However, a fixed temporal
resolution is not always optimal for different types of sound. The temporal
resolution affects not only classification accuracy but also computational
cost. This paper proposes a novel method, DiffRes, that enables differentiable
temporal resolution modeling for audio classification. Given a spectrogram
calculated with a fixed hop size, DiffRes merges non-essential time frames
while preserving important frames. DiffRes acts as a “drop-in” module between
an audio spectrogram and a classifier and can be jointly optimized with the
classification task. We evaluate DiffRes on five audio classification tasks,
using mel-spectrograms as the acoustic features, followed by off-the-shelf
classifier backbones. Compared with previous methods using the fixed temporal
resolution, the DiffRes-based method can achieve the equivalent or better
classification accuracy with at least 25% computational cost reduction. We
further show that DiffRes can improve classification accuracy by increasing the
temporal resolution of input acoustic features, without adding to the
computational cost.
In this article, the authors discuss the importance of temporal resolution in audio spectrograms and propose a novel method called DiffRes for audio classification. The temporal resolution in audio spectrograms is determined by the hop size used in the Short-Time Fourier Transform (STFT). While previous works have assumed a constant hop size, the authors argue that a fixed temporal resolution may not be optimal for different types of sound.
DiffRes addresses this issue by allowing differentiable temporal resolution modeling for audio classification. It achieves this by merging non-essential time frames in a spectrogram while preserving important frames. DiffRes acts as a module between an audio spectrogram and a classifier, enhancing the temporal resolution and reducing computational costs. Importantly, DiffRes can be jointly optimized with the classification task.
The authors conducted evaluations on five audio classification tasks using mel-spectrograms as acoustic features and off-the-shelf classifier backbones. The results showed that DiffRes-based methods achieved equivalent or better classification accuracy compared to previous methods that used fixed temporal resolution. Furthermore, the DiffRes-based approach achieved at least a 25% reduction in computational cost.
This research has multi-disciplinary implications within the field of multimedia information systems. By improving the temporal resolution of audio classification, it can enhance the accuracy and efficiency of tasks such as speech recognition, music genre classification, and sound event detection. The DiffRes method can also be applied to other areas of multimedia processing like video classification and image recognition, expanding its potential impact.
Moreover, the concepts discussed in this article are closely related to animations, artificial reality, augmented reality, and virtual realities. Audio plays a significant role in creating immersive multimedia experiences. Enhancing the classification of audio in these contexts can lead to more realistic virtual environments, interactive augmented reality applications, and improved audio synchronization in animations. The DiffRes method has the potential to enhance the audio processing capabilities in these areas, enriching the overall user experience.
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by jsendak | Jan 15, 2024 | Computer Science
The article discusses the challenges associated with providing Ultra-Reliable Low-Latency Communication (URLLC) in Industrial Internet of Things (IIoT) networks. Specifically, it focuses on the trade-off between latency and reliability in uplink communication and the limitations of existing protocols.
One approach to ensure minimal collisions in uplink communication is centralized grant-based scheduling. However, this method introduces delays in the resource request and grant process, which may not be suitable for time-sensitive processes in IIoT. On the other hand, distributed scheduling, where User Equipments (UEs) autonomously choose resources for transmission, can lead to increased collisions as traffic volume rises.
To address these challenges, the authors propose a novel scheduling framework called DISNETS. DISNETS combines the strengths of both centralized and distributed scheduling by using reinforcement learning and a feedback signal from the gNB (base station) to train UEs to optimize their uplink transmissions and minimize collisions without additional message exchange with the gNB.
DISNETS is a distributed, multi-agent adaptation of the Neural Linear Thompson Sampling (NLTS) algorithm. It leverages neural networks and combinatorial optimization to allow UEs to select the most suitable resources for transmission in parallel. The authors performed experiments to demonstrate that DISNETS outperforms other baselines in addressing URLLC in IIoT scenarios.
This research is significant as it tackles an important aspect of IIoT networks – ensuring ultra-reliable and low-latency communication for critical processes. By combining reinforcement learning and distributed scheduling, DISNETS provides a solution that minimizes collisions without introducing excessive delays. This is crucial for industries where real-time communication is vital, such as manufacturing or autonomous vehicles.
In terms of future developments and implications, further research could focus on optimizing DISNETS for specific IIoT applications and network conditions. Additionally, investigating the scalability and robustness of DISNETS when the number of UEs and network traffic increase would be valuable.
In conclusion, DISNETS offers a promising approach to address the challenges of URLLC in IIoT networks. By leveraging reinforcement learning and combining centralized and distributed scheduling, it provides a framework for UEs to autonomously optimize uplink transmissions and minimize collisions. This research has important implications for improving the reliability and latency of critical processes in IIoT applications.
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by jsendak | Jan 15, 2024 | Computer Science
The growing prevalence of online conferences and courses presents a new
challenge in improving automatic speech recognition (ASR) with enriched textual
information from video slides. In contrast to rare phrase lists, the slides
within videos are synchronized in real-time with the speech, enabling the
extraction of long contextual bias. Therefore, we propose a novel long-context
biasing network (LCB-net) for audio-visual speech recognition (AVSR) to
leverage the long-context information available in videos effectively.
Specifically, we adopt a bi-encoder architecture to simultaneously model audio
and long-context biasing. Besides, we also propose a biasing prediction module
that utilizes binary cross entropy (BCE) loss to explicitly determine biased
phrases in the long-context biasing. Furthermore, we introduce a dynamic
contextual phrases simulation to enhance the generalization and robustness of
our LCB-net. Experiments on the SlideSpeech, a large-scale audio-visual corpus
enriched with slides, reveal that our proposed LCB-net outperforms general ASR
model by 9.4%/9.1%/10.9% relative WER/U-WER/B-WER reduction on test set, which
enjoys high unbiased and biased performance. Moreover, we also evaluate our
model on LibriSpeech corpus, leading to 23.8%/19.2%/35.4% relative
WER/U-WER/B-WER reduction over the ASR model.
The Importance of Enriched Textual Information in Online Conferences and Courses
With the growing prevalence of online conferences and courses, there is a need for improved automatic speech recognition (ASR) systems that can effectively process and understand the enriched textual information from video slides. Traditional ASR systems mainly rely on rare phrase lists, but the slides within videos provide real-time synchronization with the speech, offering valuable long-context bias. This long-context information can greatly enhance the accuracy and contextual understanding of ASR systems.
The proposed long-context biasing network (LCB-net) for audio-visual speech recognition (AVSR) addresses this need by leveraging the long-context information available in videos. The LCB-net adopts a bi-encoder architecture that simultaneously models audio and long-context biasing. This approach allows for the extraction of contextual bias from the video slides, aiding in accurate speech recognition.
In addition to the bi-encoder architecture, the LCB-net also incorporates a biasing prediction module. This module uses binary cross entropy (BCE) loss to explicitly determine biased phrases in the long-context biasing. By identifying and leveraging biased phrases, the LCB-net further improves the accuracy and performance of ASR systems.
Another important aspect of the LCB-net is the dynamic contextual phrases simulation. This simulation enhances the generalization and robustness of the model by simulating various contextual scenarios and ensuring that the system is capable of handling different speech patterns and contexts.
Multi-disciplinary Nature and Relation to Multimedia Information Systems
The concepts presented in this article highlight the multi-disciplinary nature of multimedia information systems. The LCB-net combines elements from audio processing, computer vision, natural language processing, and machine learning to develop an effective AVSR system. The integration of these different disciplines allows for a comprehensive approach to speech recognition, taking into account both audio and visual cues along with contextual bias from video slides.
Furthermore, the LCB-net’s performance on the SlideSpeech corpus demonstrates its effectiveness in processing and understanding multimedia information. By leveraging the synchronized audio and video slides, the LCB-net outperforms general ASR models. This indicates the relevance of the concepts discussed in this article to the wider field of multimedia information systems.
Relation to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The concepts presented in this article, particularly the use of synchronized audio and video slides, have implications for animations, artificial reality, augmented reality, and virtual realities. In these fields, the combination of audio and visual elements is crucial for creating immersive and interactive experiences.
By leveraging contextual bias from video slides, the LCB-net can enhance the accuracy and understanding of speech in these environments. This can be particularly useful in applications where users interact with multimedia content and need accurate speech recognition, such as virtual reality simulations or augmented reality experiences with voice-controlled interfaces.
In conclusion, the proposed LCB-net offers a promising approach to improving automatic speech recognition in the context of online conferences and courses. Its ability to leverage long-context information from video slides showcases the importance of enriched textual information in multimedia systems. The multi-disciplinary nature of the concepts discussed in this article highlights their relevance to the wider field of multimedia information systems, as well as their potential applications in animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jan 15, 2024 | Computer Science
It is fascinating to see how technology can be used to solve seemingly impossible tasks. The act of reconstructing shredded banknotes using computer vision is a remarkable application that showcases the potential of this field. This article highlights the innovative technique employed in Hong Kong to collect shredded banknote pieces and the subsequent process of applying a computer vision program to reconstruct the banknotes.
The Challenge of Handling Shredded Banknotes
Shredded banknotes pose a unique challenge due to their fragmented nature. The traditional method of manually piecing together these shredded notes is time-consuming and requires great precision. It often leads to errors and incomplete reconstructions. However, by leveraging computer vision, this challenging task becomes feasible and opens up new possibilities.
Using Computer Vision for Reconstruction
Computer vision refers to a field of study that focuses on enabling computers to extract meaningful information from visual data. In the case of shredded banknotes, computer vision algorithms can analyze the unique patterns and textures present on individual fragments, helping to identify their original location within the banknote.
The reconstruction process involves several steps. First, the shredded banknote pieces are collected and sorted based on their size and shape. Next, computer vision algorithms analyze these fragments, searching for matching patterns and textures. As the algorithm identifies potential matches, it gradually assembles the shredded pieces to reconstruct the banknote.
The Role of Machine Learning
Machine learning plays a crucial role in enhancing the accuracy and efficiency of the reconstruction process. By training the computer vision algorithm on a large dataset of intact banknotes, it can learn to recognize common patterns and features found in banknotes. This knowledge enables the algorithm to make more accurate predictions during the reconstruction process, resulting in higher-quality reconstructions.
The Potential Implications
The application of computer vision in reconstructing shredded banknotes has significant implications. The ability to recover the value from shredded banknotes opens up new possibilities for financial institutions and governments.
Firstly, financial institutions can benefit from this technology by safely and efficiently disposing of old banknotes. Instead of relying on time-consuming manual methods, they can now employ computer vision algorithms to quickly reconstruct valuable banknotes.
Secondly, governments can utilize this technology to combat counterfeiting. By efficiently reconstructing counterfeit banknotes seized during investigations, authorities can gain valuable insights into the manufacturing processes used and further enhance counterfeit detection measures.
Conclusion
The technique of reconstructing shredded banknotes using computer vision is a fascinating development with practical implications. This method accelerates the process, enhances accuracy, and unlocks the potential value of shredded banknotes.
As computer vision algorithms continue to advance, we can expect further refinements in this field. The ability to reconstruct other types of shredded documents or objects may soon become a reality. Ultimately, this technology demonstrates the power of computer vision in overcoming complex challenges.
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by jsendak | Jan 15, 2024 | Computer Science
Mobile multimedia networks (MMNs) demonstrate great potential in delivering
low-latency and high-quality entertainment and tactical applications, such as
short-video sharing, online conferencing, and battlefield surveillance. For
instance, in tactical surveillance of battlefields, scalability and
sustainability are indispensable for maintaining large-scale military
multimedia applications in MMNs. Therefore, many data-driven networking
solutions are leveraged to optimize streaming strategies based on real-time
traffic analysis and resource monitoring. In addition, generative AI (GAI) can
not only increase the efficiency of existing data-driven solutions through data
augmentation but also develop potential capabilities for MMNs, including
AI-generated content (AIGC) and AI-aided perception. In this article, we
propose the framework of GAI-enabled MMNs that leverage the capabilities of GAI
in data and content synthesis to distribute high-quality and immersive
interactive content in wireless networks. Specifically, we outline the
framework of GAI-enabled MMNs and then introduce its three main features,
including distribution, generation, and perception. Furthermore, we propose a
second-score auction mechanism for allocating network resources by considering
GAI model values and other metrics jointly. The experimental results show that
the proposed auction mechanism can effectively increase social welfare by
allocating resources and models with the highest user satisfaction.
The field of multimedia information systems encompasses a wide range of disciplines, including animations, artificial reality, augmented reality, and virtual realities. This article explores the potential of using generative AI (GAI) in the context of mobile multimedia networks (MMNs), particularly in delivering low-latency and high-quality entertainment and tactical applications.
One of the key challenges in maintaining large-scale military multimedia applications in MMNs is scalability and sustainability. To address this, data-driven networking solutions are leveraged to optimize streaming strategies based on real-time traffic analysis and resource monitoring. These solutions can be further enhanced by incorporating GAI techniques, which have the ability to augment data and develop AI-generated content (AIGC) and AI-aided perception.
The framework of GAI-enabled MMNs is proposed in this article. This framework harnesses the capabilities of GAI in data and content synthesis to distribute high-quality and immersive interactive content in wireless networks. The three main features of this framework include distribution, generation, and perception.
The distribution aspect focuses on efficiently transmitting multimedia content by leveraging GAI techniques. This ensures that the content reaches users with minimal latency and high quality. The generation feature explores the potential of using GAI to create new content, enhancing the variety and richness of multimedia experiences in MMNs.
Lastly, the perception component incorporates AI-aided perception techniques to enhance the user experience. This can involve personalized content recommendations based on user preferences and context, as well as real-time adaptation of content based on user feedback.
Furthermore, the article proposes a second-score auction mechanism for allocating network resources within the GAI-enabled MMNs. This mechanism takes into account the values of GAI models and other relevant metrics to efficiently allocate resources. The experimental results demonstrate that this auction mechanism effectively increases social welfare by allocating resources and models that result in the highest user satisfaction.
In conclusion, the integration of generative AI in mobile multimedia networks holds great potential for delivering high-quality and immersive multimedia experiences. This multidisciplinary approach combines concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities to create a framework that optimizes content distribution, generation, and perception in wireless networks.
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