With the introduction of Mobile Cloud Computing (MCC), the notion of leveraging cloud-hosted components to get around mobile devices' resource constraints became more widely accepted. However, as smartphones and t...
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Currently, the performance of the police in Indonesia is often in the spotlight of the public with cases that occur, both on a national and regional scale, including personal experiences who also feel disappointed wit...
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Neural collapse provides an elegant mathematical characterization of learned last-layer representations, also known as features, and classifier weights within deep classification models. The result not only provides i...
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Neural collapse provides an elegant mathematical characterization of learned last-layer representations, also known as features, and classifier weights within deep classification models. The result not only provides insights into deep models but also catalyzes the development of new techniques for improving them. However, most of the existing empirical and theoretical studies into neural collapse center around scenarios where the number of classes is small relative to the dimensionality of the feature space. This paper introduces a generalization of neural collapse to encompass scenarios where the number of classes surpasses the dimension of feature space, which broadly occurs for language models, information retrieval systems, and face recognition applications. A key technical contribution is the introduction of the concept of softmax code, defined as a collection of points that maximizes the minimum one-vs-rest margin, to describe the arrangement of class-mean features. We provide empirical study to verify the prevalence of generalized neural collapse in practical deep neural networks. Moreover, we provide theoretical study to show that the generalized neural collapse provably occurs under an unconstrained feature model with spherical constraint, subject to specific technical conditions on feature dimension and the number of classes. Copyright 2024 by the author(s)
We consider a source node deployed in a real-time monitoring application that needs to sample a stochastic process and convey its state timely and accurately to a destination over a wireless ON/OFF channel. The source...
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As the number of video streaming platforms is growing, the risk factor associated with illegal and inappropriate content streaming is increasing exponentially. Therefore, monitoring such content is essential. Many res...
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Recent dual in-line memory modules (DIMMs) are starting to support processing-in-memory (PIM) by associating their memory banks with processing elements (PEs), allowing applications to overcome the data movement bottl...
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ISBN:
(数字)9798350326581
ISBN:
(纸本)9798350326598
Recent dual in-line memory modules (DIMMs) are starting to support processing-in-memory (PIM) by associating their memory banks with processing elements (PEs), allowing applications to overcome the data movement bottleneck by offloading memory-intensive operations to the PEs. Many highly parallel applications have been shown to benefit from these PIM-enabled DIMMs, but further speedup is often limited by the huge overhead of inter-PE collective communication. This mainly comes from the slow CPU-mediated inter-PE communication methods, making it difficult for PIM-enabled DIMMs to accelerate a wider range of applications. Prior studies have tried to alleviate the communication bottleneck, but they lack enough flexibility and performance to be used for a wide range of applications. In this paper, we present PID-Comm, a fast and flexible inter-PE collective communication framework for commodity PIM-enabled DIMMs. The key idea of PID-Comm is to abstract the PEs as a multi-dimensional hypercube and allow multiple instances of inter-PE collective communication between the PEs belonging to certain dimensions of the hypercube. Leveraging this abstraction, PID-Comm first defines eight interPE collective communication patterns that allow applications to easily express their complex communication patterns. Then, PIDComm provides high-performance implementations of the interPE collective communication patterns optimized for the DIMMs. Our evaluation using 16 UPMEM DIMMs and representative parallel algorithms shows that PID-Comm greatly improves the performance by up to $5.19 \times$ compared to the existing inter-PE communication implementations. The implementation of PIDComm is available at https://***/AIS-SNU/PID-Comm.
Dengue fever remains a serious threat to public health in Bangladesh and in many other tropical and subtropical locations. The prediction of dengue risk zones depends on early and accurate prediction of dengue risk zo...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Dengue fever remains a serious threat to public health in Bangladesh and in many other tropical and subtropical locations. The prediction of dengue risk zones depends on early and accurate prediction of dengue risk zones. This study follows machine learning techniques to analyze weather data including temperature, humidity, precipitation, and wind speed to predict dengue risk levels. It follows a methodology of data preprocessing, model training using accuracy, precision, recall and F1 score. Several models including Support Vector Machine, Random Forest as well as Gradient Boosting have been applied and compared. The results have shown that the Gradient Boosting has outperformed all the models with an accuracy of 99.54%. The study shows how machine learning can improve dengue risk zone prediction and support evidence-based public health decisions.
Monocular depth estimation is an important step in many downstream tasks in machine vision. We address the topic of estimating monocular depth from defocus blur which can yield more accurate results than the semantic ...
Monocular depth estimation is an important step in many downstream tasks in machine vision. We address the topic of estimating monocular depth from defocus blur which can yield more accurate results than the semantic based depth estimation methods. The existing monocular depth from defocus techniques are sensitive to the particular camera that the images are taken from. We show how several camera-related parameters affect the defocus blur using optical physics equations and how they make the defocus blur depend on these parameters. The simple correction procedure we propose can alleviate this problem which does not require any retraining of the original model. We created a synthetic dataset which can be used to test the camera independent performance of depth from defocus blur models. We evaluate our model on both synthetic and real datasets (DDFF12 and NYU depth V2) obtained with different cameras and show that our methods are significantly more robust to the changes of cameras. Code: https://***/sleekEagle/defocus_***
This paper proposes a containment control law for a heterogeneous multi-agent system (MAS) by using the binary relative position measurements of the agents and the targets. Unlike the existing methods, this approach r...
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Data security is of the utmost importance in wireless sensor networks due to the massive amounts of data collected and sent between nodes. An improved security method for wireless sensor networks is presented in this ...
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ISBN:
(纸本)9798350375237
Data security is of the utmost importance in wireless sensor networks due to the massive amounts of data collected and sent between nodes. An improved security method for wireless sensor networks is presented in this paper. This mechanism takes into account the application, the security level, and the bit error rate simultaneously. In order to provide the user with the ability to select between unsecure and secure modes, we made use of reserved bits of the frame control field that are contained within the communication header. The Secured and Intelligent Data Transmission Protocol (SIDTP) is the name of the unified perceptual information transmission model that is established in this research. Its purpose is to compensate for the unequal distribution of cognitive radio users across different wireless LANs. For the purpose of determining whether or not the suggested scheme is effective, the proposed model will be cross-validated with the traditional data transmission model known as Wireless Data Transmission Protocol (WDTP). The approach has the potential to optimize the utilization of spectrum resources in an efficient manner. In addition, to help lessen the risks connected with wireless networks, this study lays out a technique for transmitting data using cyclic delay full diversity sensing information coding. In order to evaluate the performance of the system against a variety of correlation coefficients, we developed a system simulation model. The result of the study showed that cyclic delay diversity systems had overall interference intensity similar to frequency division duplex as well as time division duplex. As an added bonus, this enhances its anti-jamming attack capabilities. The proposed system has been planned and implemented, and significant trials have been carried out in an industrial setting. Additionally, a prototype of the system has been designed. Compared to conventional communication interfaces that are only equipped with a single wireless interf
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