A significant proportion of noticeable improvement in machine learning architectures actually benefits from the consistent inspiration of the way human learning [1]. For instance, curriculum learning [2] is inspired b...
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A significant proportion of noticeable improvement in machine learning architectures actually benefits from the consistent inspiration of the way human learning [1]. For instance, curriculum learning [2] is inspired by highly organized human education systems, i.e., training the algorithms with easy samples first and gradually transforming to the hard examples can contribute to faster convergence and lower generalization error.
University Course Timetabling Problem (UCTP) is a significant resource allocation challenge with NP-hard characteristics. As problem sizes increase, finding an optimal solution becomes increasingly complex. To address...
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Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the curre...
On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the...
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Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multip...
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Identifying pavement damage is a crucial component in road maintenance and infrastructure management. Prompt detection and corrective action of pavement defects can prevent severe deterioration, maintain safety, and p...
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Music classification is a fundamental task in the field of Music Information Retrieval. This paper focuses on composer classification, a specific task within music classification. Compressive techniques are commonly e...
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Public clouds favor sharing of storage resources,in which many tenants acquire bandwidth and storage capacity from a shared storage *** provide high availability,data are often encoded to provide fault tolerance with ...
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Public clouds favor sharing of storage resources,in which many tenants acquire bandwidth and storage capacity from a shared storage *** provide high availability,data are often encoded to provide fault tolerance with low storage *** this,efficiently organizing an encoded storage system for shared I/Os is critical for application *** is usually hard to achieve as different applications have different stripe configurations and fault tolerance *** this paper,we first study the block trace from the Alibaba cloud,and find that I/O patterns of modern applications prefer the resource sharing *** on this,we propose a globally shared resource paradigm for encoded storage system in the public *** globally shared resource paradigm can provide balanced load and fault tolerance for numerous disk pool sizes and arbitrary application stripe ***,we demonstrate with two case studies that our theory can help address the device-specific problems of HDD and SSD RAID arrays with slight modifications:comparing the existing resource partition and resource sharing methods,our theory can promote the rebuild speed of the HDD RAID arrays by 2.5,and reduce the P99 tail latency of the SSD arrays by up to two orders of magnitude.
360°videos enable viewers to watch freely from different directions but inevitably prevent them from perceiving all the helpful *** mitigate this problem,picture-in-picture(PIP)guidance was proposed using preview...
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360°videos enable viewers to watch freely from different directions but inevitably prevent them from perceiving all the helpful *** mitigate this problem,picture-in-picture(PIP)guidance was proposed using preview windows to show regions of interest(ROIs)outside the current view *** identify several drawbacks of this representation and propose a new method for 360° film watching called *** enhances traditional PIP by adaptively arranging preview windows with changeable view ranges and *** addition,AdaPIP incorporates the advantage of arrow-based guidance by presenting circular windows with arrows attached to them to help users locate the corresponding ROIs more *** also adapted AdaPIP and Outside-In to HMD-based immersive virtual reality environments to demonstrate the usability of PIP-guided approaches beyond 2D *** user experiments on 2D screens,as well as in VR environments,indicate that AdaPIP is superior to alternative methods in terms of visual experiences while maintaining a comparable degree of immersion.
Visual sensors are indispensable for automatic vehicles, to achieve comprehensive environmental perception for navigation, but their deteriorated performance in harsh illuminations largely sets back the practical use ...
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Visual sensors are indispensable for automatic vehicles, to achieve comprehensive environmental perception for navigation, but their deteriorated performance in harsh illuminations largely sets back the practical use of autonomous driving technologies. A promising solution is to use a bio-inspired event sensor that asynchronously records the intensity changes with high sensitivity, fast response, and large dynamic range, which assists situational awareness of moving vehicles in harshly lit scenarios. However, the sensing of event sensors comes with heavy noise and sparse signals, due to either severe photon starvation or limited acquisition bandwidth. In this paper, we propose an approach for real-time sketching of the harshly lit driving environment (RIDE), to outline the driving surroundings from noisy sporadic measurements. We address confronted challenges as follows: (i) map the raw event signals into a low dimensional space and cluster the features to depict the spatial-temporal correlation within raw events;(ii) design a general inference network to construct continuous motion fields of the scene from the encoded features of noisy sporadic raw measurements;(iii) construct the pseudo-ground-truth via the unsupervised motion compensation as the label of the above network learning, achieving real-time inference. Our approach is experimentally validated on real traffic data and displays high-fidelity perception capability for extremely dark scenes and scenarios with high dynamic range. Also, we investigate RIDE's effectiveness in the downstream task—detection of traffic participants. In a nutshell, the proposed RIDE provides high-fidelity sensing of harshly lit environments and lays the foundation for the all-day visual navigation of autonomous vehicles. IEEE
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