by jsendak | Jan 13, 2024 | Computer Science
This paper presents a novel framework that aims to address the important issue of worker biomechanical risk during lifting tasks. By combining online human state estimation, action recognition, and motion prediction, this framework enables early assessment and prevention of potential risks. This is achieved by leveraging the NIOSH index for online risk assessment, making it suitable for real-time applications.
The framework begins by retrieving the human state using inverse kinematics/dynamics algorithms from wearable sensor data. This allows for a detailed understanding of the worker’s posture and movements during the lifting task. Importantly, the human action recognition and motion prediction components of the framework use an LSTM-based Guided Mixture of Experts architecture, which is trained offline and can be inferred online.
By accurately recognizing the actions being performed by the worker, the framework is able to break down a single lifting activity into a series of continuous movements. This is crucial for applying the Revised NIOSH Lifting Equation, which provides a standardized method for assessing risk. By quantifying the biomechanical stress on the worker’s body, the framework can provide valuable insights into potential risks.
In addition to assessing risk during the lifting task, the framework also enables the anticipation of future risks through motion prediction. By analyzing the historical data and understanding the patterns and trends in the worker’s movements, this framework can provide early warnings of potential risks that may arise in the future. This proactive approach to risk prevention can significantly enhance worker safety.
An interesting aspect of this framework is the inclusion of a haptic actuator embedded in the wearable system. This actuator can alert the worker of potential risks in real-time, acting as an active prevention device. By providing tactile feedback, the actuator can help the worker make adjustments to their posture or technique to minimize the risk of injury.
To validate the performance of the proposed framework, real lifting tasks were executed while the subjects were equipped with the iFeel wearable system. This allowed for the collection of real-world data and enabled a thorough evaluation of the framework’s effectiveness.
This framework has significant potential in improving worker safety during lifting tasks. By combining online human state estimation, action recognition, and motion prediction, this framework provides a comprehensive solution for assessing and preventing biomechanical risks. The integration of a haptic actuator further enhances the capabilities of this system. Future research could focus on refining the accuracy of the human state estimation, exploring additional risk assessment methods, and evaluating the performance of this framework in different workplace scenarios.
Read the original article
by jsendak | Jan 13, 2024 | Computer Science
In this article, the authors introduce the concept of domain generalization into automatic sleep staging, which is important for sleep assessment and disorder diagnosis. They highlight that most existing methods for sleep staging rely on specific datasets and cannot be easily generalized to unseen datasets. To address this issue, they propose the task of generalizable sleep staging and present a framework called SleepDG to achieve this.
The authors draw inspiration from existing domain generalization methods and adopt the idea of feature alignment. They argue that considering both local salient features and sequential features is crucial for accurate sleep staging. To tackle this, they propose a Multi-level Feature Alignment approach that combines epoch-level and sequence-level feature alignment to learn domain-invariant feature representations.
To align the feature distribution of each sleep epoch among different domains, the authors design an Epoch-level Feature Alignment method. This helps to ensure that the features extracted from individual sleep epochs are consistent across datasets. Additionally, they introduce a Sequence-level Feature Alignment technique that minimizes the discrepancy of sequential features between different domains.
The proposed SleepDG framework is evaluated on five public datasets and achieves state-of-the-art performance in sleep staging. This demonstrates its effectiveness in improving the generalization ability of sleep staging models to unseen datasets.
Overall, the authors’ work on introducing domain generalization into automatic sleep staging is significant as it addresses an important limitation of existing methods. By leveraging feature alignment techniques, SleepDG provides a promising solution for improving the generalization ability of sleep staging models. Future research in this area could explore additional techniques for domain generalization and investigate the application of SleepDG in real-world clinical settings.
Read the original article
by jsendak | Jan 13, 2024 | Computer Science
The development of U.S. Army and NATO data link systems has been crucial in enhancing communication and coordination among military units. These systems have greatly improved situational awareness and decision-making capabilities on the battlefield. However, as technology continues to evolve, the future of data link systems lies in the integration, generalization, multifunctionality, and high security.
Integration
Future data link systems will focus on integrating various information sources and platforms. This integration will allow for a seamless exchange of information between different military units, as well as with allies who use different communication systems. The ability to efficiently share real-time data across different platforms will enhance interoperability and coordination during joint operations.
Generalization
To meet the demands of modern warfare, data link systems are expected to become more generalized. This means that they will be designed to support a wide range of missions and scenarios. Whether it is urban combat or unconventional warfare, future data link systems will need to adapt and provide timely and relevant information to decision-makers in any situation.
Multifunctionality
Data link systems will not only transmit and receive information but will also possess advanced analytical capabilities. These advanced analytics will enable the system to process and interpret large amounts of data, providing commanders with actionable intelligence. By analyzing data in real-time, commanders will be able to make informed decisions quickly and effectively.
High Security
In an era of increasing cyber threats, data link systems must prioritize high-security measures. Robust encryption and authentication protocols will ensure that sensitive information is protected from unauthorized access. Additionally, measures to detect and mitigate cyber attacks will be crucial to maintaining the integrity and reliability of data link systems.
The Unit-level Combat System Architecture
Proposed here is a unit-level combat system architecture based on the global combat cloud. This architecture enables flexible scheduling of global combat resources, maximizing overall combat effectiveness. At the heart of this solution lies the development of intelligent data link systems, which will provide strong information support for future urban unit-level warfare.
Intelligent data link systems will integrate seamlessly into the global combat cloud, enabling real-time information sharing and coordination among allied forces. These systems will not only provide situational awareness but also facilitate the rapid decision-making process during combat operations.
By leveraging advanced technologies, such as artificial intelligence and machine learning, intelligent data link systems will be able to analyze vast amounts of data and provide commanders with actionable intelligence. This will significantly enhance their ability to understand the battlefield and make critical decisions.
In conclusion, the future of data link systems lies in their integration, generalization, multifunctionality, and high security. As technology continues to advance, military forces must adapt their communication systems to meet the evolving demands of modern warfare. The proposed unit-level combat system architecture based on the global combat cloud, powered by intelligent data link systems, will undoubtedly revolutionize how military operations are conducted in the future.
Read the original article
by jsendak | Jan 13, 2024 | Computer Science
Architectures that incorporate Computing-in-Memory (CiM) using emerging non-volatile memory (NVM) devices have become strong contenders for deep neural network (DNN) acceleration due to their impressive energy efficiency.
This statement immediately highlights the significance of the topic being discussed. The use of non-volatile memory devices in computing architectures is gaining attention for its potential to accelerate deep neural networks while also reducing energy consumption. This indicates that the advancements in non-volatile memory technology are opening up new possibilities in the field of deep learning.
Yet, a significant challenge arises when using these emerging devices: they can show substantial variations during the weight-mapping process. This can severely impact DNN accuracy if not mitigated.
The article reveals a crucial challenge associated with using emerging non-volatile memory devices – their susceptibility to variations during the weight-mapping process. These variations can have a detrimental effect on the accuracy of deep neural networks if not properly addressed. Consequently, this highlights the need for effective techniques to mitigate these variations and ensure reliable and accurate DNN inference.
A widely accepted remedy for imperfect weight mapping is the iterative write-verify approach, which involves verifying conductance values and adjusting devices if needed.
The article identifies the iterative write-verify approach as a commonly adopted solution for addressing imperfect weight mapping. This approach involves verifying conductance values and making necessary adjustments to the devices to ensure accurate weight mapping. It suggests that this iterative process can help improve the accuracy of DNNs implemented using these emerging non-volatile memory devices.
In all existing publications, this procedure is applied to every individual device, resulting in a significant programming time overhead.
One key limitation highlighted in the article is the time overhead associated with applying the iterative write-verify procedure to every individual device. The existing publications seem to follow this approach, which can lead to significant programming time overhead. This indicates the necessity for a more efficient technique that can reduce the time required for write-verify treatment without compromising DNN accuracy.
In our research, we illustrate that only a small fraction of weights need this write-verify treatment for the corresponding devices and the DNN accuracy can be preserved, yielding a notable programming acceleration.
The researchers present their findings, demonstrating that not all weights require the write-verify treatment for the corresponding devices. By identifying that only a small fraction of weights necessitate this procedure, they propose that DNN accuracy can be preserved while achieving significant programming acceleration. This implies that they have discovered a potential solution to mitigate the programming time overhead associated with the iterative write-verify approach.
Building on this, we introduce USWIM, a novel method based on the second derivative. It leverages a single iteration of forward and backpropagation to pinpoint the weights demanding write-verify.
The authors introduce their novel method called USWIM, which builds upon their previous research. This novel approach utilizes the second derivative and employs a single iteration of forward and backpropagation to specifically identify the weights requiring write-verify treatment. By implementing this method, they aim to further reduce programming time by efficiently pinpointing the specific weights that need attention.
Through extensive tests on diverse DNN designs and datasets, USWIM manifests up to a 10x programming acceleration against the traditional exhaustive write-verify method, all while maintaining a similar accuracy level.
The researchers provide evidence of the effectiveness of their USWIM technique by conducting extensive tests on various deep neural network designs and datasets. They indicate that their method achieves remarkable programming acceleration, up to 10 times faster, in comparison to the traditional exhaustive write-verify method. Furthermore, they highlight that this acceleration is achieved without compromising the accuracy of the DNNs. This suggests that their approach presents a significant improvement over existing methods in terms of computational efficiency.
Furthermore, compared to our earlier SWIM technique, USWIM excels, showing a 7x speedup when dealing with devices exhibiting non-uniform variations.
The authors make an additional comparison between their previous SWIM technique and the newly introduced USWIM method. They reveal that USWIM outperforms SWIM by achieving a 7 times speedup when handling devices with non-uniform variations. This showcases the superiority of the USWIM technique in addressing the challenges posed by devices with varying characteristics.
Overall, this article emphasizes the challenges related to emerging non-volatile memory devices in deep neural network acceleration and highlights the need for efficient programming approaches. The researchers’ novel method, USWIM, shows promising results by significantly reducing programming time while preserving DNN accuracy. This research contributes to the advancement of Computing-in-Memory architectures and opens up possibilities for accelerating deep neural networks using non-volatile memory devices.
Read the original article
by jsendak | Jan 13, 2024 | Computer Science
In this article, the authors propose a method for optimizing mutual coupling functions to achieve fast and global synchronization between weakly coupled limit-cycle oscillators. Synchronization of oscillators is an important phenomenon in various fields such as physics, biology, and engineering. Understanding and controlling synchronization dynamics are crucial for many applications.
Phase Reduction and Low-dimensional Representation
The authors base their method on phase reduction, which provides a concise low-dimensional representation of the synchronization dynamics of coupled oscillators. Phase reduction has been a powerful tool in studying the collective behavior of oscillatory systems and has been widely used to simplify the analysis of synchronization.
Optimization for Identical Oscillators
The proposed method begins by describing the optimization process for a pair of identical oscillators. This serves as a foundation for understanding the more general case of slightly nonidentical oscillators. By optimizing the coupling function’s functional form and amplitude, the authors aim to minimize the average convergence time while ensuring a constraint on the total power.
Numerical Simulations and Comparisons
To validate their method, the authors perform numerical simulations using the FitzHugh-Nagumo and Rössler oscillators as examples. They compare the performance of the coupling function optimized by their proposed method with previous methods. Through these simulations, they demonstrate that the optimized coupling function can achieve global synchronization more efficiently.
Expert Analysis and Insights
This article presents a method that addresses an important problem in the field of coupled oscillators. The ability to efficiently achieve synchronization can have significant implications for various real-world applications. By leveraging phase reduction and optimization techniques, the authors provide a systematic approach for designing coupling functions that promote fast and global synchronization.
One potential area for further research is the extension of this method to larger networks of oscillators. While the article focuses on a pair of oscillators, the same principles can potentially be applied to systems with multiple oscillators. Exploring the scalability and robustness of the proposed method in larger networks would be an interesting direction for future studies.
Additionally, it would be valuable to investigate the effect of different types of coupling functions on synchronization dynamics. The article primarily focuses on optimizing the functional form and amplitude of the coupling function, but there are various other factors that could influence synchronization, such as delay and frequency-dependent coupling. Understanding how different types of coupling functions impact synchronization could provide further insights into the dynamics of coupled oscillators.
Conclusion
In conclusion, this article presents a method for optimizing mutual coupling functions to achieve fast and global synchronization between weakly coupled limit-cycle oscillators. By leveraging phase reduction and optimization techniques, the authors demonstrate improved efficiency in achieving synchronization compared to previous methods. This research contributes to our understanding of synchronization dynamics and opens up new possibilities for designing and controlling oscillatory systems in various fields.
Read the original article
by jsendak | Jan 13, 2024 | Computer Science
Generalized Category Discovery (GCD) for Imbalanced Data
The article discusses a challenging and practical problem known as Imbalanced Generalized Category Discovery (ImbaGCD) in the context of machine learning and computer vision. GCD aims to identify known and unknown categories in an unlabeled dataset using prior knowledge from a labeled set. However, previous research assumes that the frequency of occurrence for each category is equal in the unlabeled data, which is not representative of real-world scenarios.
The Long-Tailed Property of Visual Classes
The article highlights the long-tailed property of visual classes, where known or common categories are more frequent than unknown or uncommon ones in nature. For example, in image recognition tasks, we encounter everyday objects more often than rare or specialized objects. This characteristic poses a challenge for GCD algorithms, as they are not optimized to handle imbalanced distributions of class occurrences.
Introducing ImbaGCD: An Optimal Transport-Based Framework
To address the aforementioned issues, the authors propose a novel framework called ImbaGCD. It leverages an optimal transport-based expectation maximization approach to achieve generalized category discovery by aligning the marginal class prior distribution. In simple terms, ImbaGCD aims to balance the representation of known and unknown categories in the unlabeled data.
Estimating Imbalanced Class Prior Distribution
ImbaGCD also incorporates a systematic mechanism for estimating the imbalanced class prior distribution under the GCD setup. This step is crucial because it allows the algorithm to appropriately allocate resources to discover both known and unknown categories, taking into account the imbalanced nature of the dataset.
Evaluating ImbaGCD’s Effectiveness
To validate the proposed ImbaGCD framework, comprehensive experiments were conducted on benchmark datasets such as CIFAR-100 and ImageNet-100. The results demonstrate that ImbaGCD surpasses previous state-of-the-art GCD methods by achieving an improvement of approximately 2-4% on CIFAR-100 and 15-19% on ImageNet-100. These performance gains indicate the superior effectiveness of ImbaGCD in solving the challenging problem of imbalanced GCD.
Expert Commentary:
The ImbaGCD framework addresses a crucial limitation in existing GCD methods, which assume balanced class distributions in the unlabeled data. In real-world scenarios, it is more likely to encounter known or common classes, meaning that imbalanced class distributions are prevalent. By incorporating an optimal transport-based approach and estimating the imbalanced class prior distribution, ImbaGCD provides a valuable solution to the problem.
This research also highlights the significance of addressing the long-tailed property of visual classes. Many applications, such as object recognition and image understanding, heavily rely on accurately identifying rare or uncommon objects. Therefore, developing effective algorithms that can discover and classify both known and unknown categories in imbalanced datasets is a critical step towards advancing computer vision tasks.
Moreover, the performance improvements demonstrated by ImbaGCD on benchmark datasets like CIFAR-100 and ImageNet-100 indicate its relevance and potential for real-world applications. The ability to achieve higher accuracy in generalized category discovery can contribute to advancements in numerous domains, including autonomous systems, healthcare diagnostics, and surveillance systems.
In conclusion, the ImbaGCD framework presents an optimized solution for tackling imbalanced Generalized Category Discovery tasks. By considering the imbalanced class prior distribution and leveraging an optimal transport-based approach, ImbaGCD surpasses previous methods and demonstrates superior effectiveness in solving the challenging problem of imbalanced GCD. Further advancements in this area will contribute to the development of more robust and accurate computer vision systems.
Read the original article
