Wheeler DFAs (WDFAs) are a sub-class of finite-state automata which is playing an important role in the emerging field of compressed data structures: as opposed to general automata, WDFAs can be stored in just log s +...
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ISBN:
(纸本)9781665478939
Wheeler DFAs (WDFAs) are a sub-class of finite-state automata which is playing an important role in the emerging field of compressed data structures: as opposed to general automata, WDFAs can be stored in just log s + O(1) bits per edge, s being the alphabet's size, and support optimal-time pattern matching queries on the substring closure of the language they recognize. An important step to achieve further compression is minimization. When the input A is a general deterministic finite-state automaton (DFA), the state-of-the-art is represented by the classic Hopcroft's algorithm, which runs in O(vertical bar A vertical bar log vertical bar A vertical bar) time. This algorithm stands at the core of the only existing minimization algorithm for Wheeler DFAs, which inherits its complexity. In this work, we show that the minimum WDFA equivalent to a given input WDFA can be computed in linear O(vertical bar A vertical bar) time. When run on de Bruijn WDFAs built from real DNA datasets, an implementation of our algorithm reduces the number of nodes from 14% to 51% at a speed of more than 1 million nodes per second.
With the rapid development of image processing technology, there is an increasing demand for customized, high-precision portrait graphics. The clarity and level of detail in these images directly affect tasks such as ...
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In deep learning, quantization is employed to tackle deployment challenges of neural networks in resource-limited environments like mobile and edge devices. Traditional full-precision (32-bit floating-point) models, w...
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ISBN:
(纸本)9798350383638;9798350383645
In deep learning, quantization is employed to tackle deployment challenges of neural networks in resource-limited environments like mobile and edge devices. Traditional full-precision (32-bit floating-point) models, while effective, are restricted by their high memory and computational demands, limiting their use in devices with constrained computational power and resources. To address this problem, we present a neural network quantization methodology that is primarily geared towards resource-constrained devices and inputs. Our methodology focuses on optimizing network performance for resource-limited settings, featuring a unique forward quantization function. This function employs the Minimize Discretization Error (MDE) technique to reduce information loss during quantization, particularly targeting near-zero weights, while maintaining computational efficiency and model accuracy. Additionally, we integrate the Arctangent Soft Round (ASR) method in the forward process to further smooth the data in low-bit quantization scenarios. Finally, we design a progressive quantization method, progressively transitioning from full precision to low bits, stabilizing the network at each quantization level. Tested on a resource-efficient variant of MobileNetV2 and low-resolution input data (CIFAR10/100), our method surpasses most contemporary techniques in terms of lightweight model performance. Through progressive quantization, our 4-bit quantized model even exceeds the accuracy of its full-precision counterpart as evidenced by our ablation studies.
In recent years, due to the enormous data utility everywhere, the need for datacompression grows drastically in almost all fields of science and engineering. A few examples of datacompression include such as mobile ...
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A light field is usually represented as a set of multi-view images captured from a two-dimensional (2-D) array of viewpoints and requires a large amount of data compared with a standard 2-D image. We propose a 2-D com...
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Acoustic emission based Structural Health Monitoring (SHM) sensors are installed on structures for continuous data collection to determine anomalies (e.g., crack, corrosion) and prevent unexpected failures. Due to the...
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As an essential branch of web service applications, the location-based service (LBS) plays an irreplaceable role in our daily lives. Usually, the LBS is time-sensitive, which requires the system to process trajectory ...
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ISBN:
(纸本)9781665473309
As an essential branch of web service applications, the location-based service (LBS) plays an irreplaceable role in our daily lives. Usually, the LBS is time-sensitive, which requires the system to process trajectory data in a real-time manner. Due to the sensitivity of trajectory data, LBS services may violate users' privacy. Furthermore, trajectory compression plays a crucial auxiliary role in analyzing and mining massive trajectory raw data such as trajectory clustering and trajectory similarity calculation and can help keep users' privacy. In other words, trajectory compression serves as the prerequisite for privacypreserved trajectory data mining, which retains points with highinformation content and removes redundant approximate points with low information value under the premise of protecting users' privacy. We can speed up the applications' response speed and save computing resources if we take advantage of trajectory compression and provide lightweight data support for big-datadriven web page extraction, convenient for fast and accurate response. Unfortunately, trajectory compression's current real-time processing capacity is still not big enough and not cost-effective. In terms of the implementation principle, most of the existing works are micro-batch processing. Consequently, the system will overly consume resources and respond with a high latency with the trajectory data inputting. In addition, it is difficult for users to understand and set compression parameters correctly. In this context, we propose an algorithm to incrementally compress the trajectory in real-time based on the azimuth change, and two kinds of user-perceivable parameters are proposed to facilitate real-time specific compression. For verification, our study uses real-world data sets, such as GeoLife Trajectory data. We also found that compared with the current OPW-TR algorithm with better all-around performance, our algorithm dramatically improves the processing speed with a minimal l
Ships and submarines, being the important and critical assets of Naval Forces, it becomes essential to convey tactical information to them during operation under hostile environment. Principally, Very Low Frequency (V...
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Coding algorithms usually compress independently the images of a collection, in particular when the correlation between them only resides at the semantic level, i.e., information related to the high-level image conten...
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ISBN:
(数字)9781665496209
ISBN:
(纸本)9781665496209
Coding algorithms usually compress independently the images of a collection, in particular when the correlation between them only resides at the semantic level, i.e., information related to the high-level image content. In this work, we propose a coding solution able to exploit this semantic redundancy to decrease the storage cost of data collections. First we introduce the multi-item compression framework. Then we derive a loss term to shape the latent space of a variational auto-encoder so that the latent vectors of semantically identical images can be aligned. Finally, we experimentally demonstrate that this alignment leads to a more compact representation of the data collection.
Based on the analysis of JPG algorithm, this paper constructs a compression model of electromagnetic spectrum monitoring data based on JPG algorithm, and implements it with Python programming language. Finally, the co...
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