Low back pain is a leading cause of disability globally, is often associated with degenerative lumbar spine conditions. Accurate diagnosis of these conditions is critical but challenging due to the subjective nature o...
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This paper proposes a new method to improve cache placement for various rendering algorithms using caching techniques. The proposed method comprises two stages. The first stage computes an initial cache distribution b...
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Offline reinforcement learning(RL) has gathered increasing attention in recent years, which seeks to learn policies from static datasets without active online exploration. However, the existing offline RL approaches o...
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Offline reinforcement learning(RL) has gathered increasing attention in recent years, which seeks to learn policies from static datasets without active online exploration. However, the existing offline RL approaches often require a large amount of pre-collected data and hence are hardly implemented by a single agent in practice. Inspired by the advancement of federated learning(FL), this paper studies federated offline reinforcement learning(FORL),whereby multiple agents collaboratively carry out offline policy learning with no need to share their raw ***, a straightforward solution is to simply retrofit the off-the-shelf offline RL methods for FL, whereas such an approach easily overfits individual datasets during local updating, leading to instability and subpar performance. To overcome this challenge, we propose a new FORL algorithm, named model-free(MF)-FORL, that exploits novel“proximal local policy evaluation” to judiciously push up action values beyond local data support, enabling agents to capture the individual information without forgetting the aggregated knowledge. Further, we introduce a model-based variant, MB-FORL, capable of improving the generalization ability and computational efficiency via utilizing a learned dynamics model. We evaluate the proposed algorithms on a suite of complex and high-dimensional offline RL benchmarks, and the results demonstrate significant performance gains over the baselines.
Text style transfer aims to rephrase a sentence to match the desired style while retaining the original content. As a controllable text generation task, mainstream approaches use content-independent style embedding as...
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Text style transfer aims to rephrase a sentence to match the desired style while retaining the original content. As a controllable text generation task, mainstream approaches use content-independent style embedding as control variables to guide stylistic generation. Nonetheless, stylistic properties are contextsensitive even under the same style. For example, “delicious” and “helpful” convey positive sentiments,although they are more likely to describe food and people, respectively. Therefore, desired style signals must vary with the content. To this end, we propose a memory-enhanced transfer method, which learns fine-grained style representation concerning content to assist transfer. Rather than employing static style embedding or latent variables, our method abstracts linguistic characteristics from training corpora and memorizes subdivided content with the corresponding style representations. The style signal is dynamically retrieved from memory using the content as a query, providing a more expressive and flexible latent style space. To address the imbalance between quantity and quality in different content, we further introduce a calibration method to augment memory construction by modeling the relationship between candidate *** results obtained using three benchmark datasets confirm the superior performance of our model compared to competitive approaches. The evaluation metrics and case study also indicate that our model can generate diverse stylistic phrases matching context.
Obesity is a global health crisis projected to affect one billion people worldwide by 2030. Previous research has emphasized the role of food cues in print media and television as contributing factors to the obesity e...
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Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inher...
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Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.
The private set intersection(PSI) protocol allows two parties holding a set of integers to compute the intersection of their sets without revealing any additional information to each other. The unbalanced PSI schemes ...
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The private set intersection(PSI) protocol allows two parties holding a set of integers to compute the intersection of their sets without revealing any additional information to each other. The unbalanced PSI schemes consider a specific setting where a client holds a small set of the size n and a server holds a much larger set of the size m(n ■ m). The communication overhead of state-of-the-art balanced PSI schemes is O(m + n) and the unbalanced PSI schemes are O(nlogm). In this paper, we propose a novel secure unbalanced PSI protocol based on a hash proof system. The communication complexity of our protocol grows only linearly with the size of the small set. In other words, our protocol achieves communication overhead of O(n). We test the performance on a personal computer(PC) machine with a local area network(LAN)setting for the network. The experimental results demonstrate that the client only takes 2.01 s of online computation, 4.27 MB of round trip communication to intersect 1600 pieces of 32-bit integers with 220pieces of 32-bit integers with the security parameter λ = 512. Our protocol is efficient and can be applied to resource-constrained devices, such as cell phones.
Age prediction has become an important computer Vision task. Although this task requires the age of an individual to be predicted from a given face, research has shown that it is more intuitive and easier for humans t...
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Automated machine learning(AutoML) has achieved remarkable success in automating the non-trivial process of designing machine learning *** the focal areas of AutoML,neural architecture search(NAS) stands out,aiming to...
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Automated machine learning(AutoML) has achieved remarkable success in automating the non-trivial process of designing machine learning *** the focal areas of AutoML,neural architecture search(NAS) stands out,aiming to systematically explore the complex architecture space to discover the optimal neural architecture configurations without intensive manual *** has demonstrated its capability of dramatic performance improvement across a large number of real-world *** core components in NAS methodologies normally include(ⅰ) defining the appropriate search space,(ⅱ)designing the right search strategy and(ⅲ) developing the effective evaluation *** early NAS endeavors are characterized via groundbreaking architecture designs,the imposed exorbitant computational demands prompt a shift towards more efficient paradigms such as weight sharing and evaluation estimation,***,the introduction of specialized benchmarks has paved the way for standardized comparisons of NAS ***,the adaptability of NAS is evidenced by its capability of extending to diverse datasets,including graphs,tabular data and videos,etc.,each of which requires a tailored *** paper delves into the multifaceted aspects of NAS,elaborating on its recent advances,applications,tools,benchmarks and prospective research directions.
Melanoma is a lethal type of skin cancer that has become very common due to its high metastatic rate. Therefore, accurate and timely diagnosis plays a vital role in a patient’s effective treatment and recovery. Melan...
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Melanoma is a lethal type of skin cancer that has become very common due to its high metastatic rate. Therefore, accurate and timely diagnosis plays a vital role in a patient’s effective treatment and recovery. Melanoma dermoscopic images provide a detailed analysis of pigmented lesions. Traditional manual segmentation by dermatologists has limitations such as inter-observer variability, time consumption, and human error. The deep learning (DL) techniques enhance diagnosis by automating lesion detection and segmentation. In this work, a DL framework for the localization of melanoma lesions using dermoscopic images is presented. The proposed framework utilizes an encoder-decoder architecture inspired by the UNet model. The encoder-decoder architecture enables effective feature extraction and spatial information preservation. The encoder part efficiently captures hierarchical features from the input data. At the same time, the decoder part reconstructs the spatial details, leading to accurate segmentation results. Therefore, the proposed framework takes advantage of the capability of the encoder-decoder architecture and employs it in the depth of 3. Extensive experiments are conducted to determine the optimal set of hyperparameters and architecture. The performance of the proposed framework is assessed on unseen samples via a cross-database validation scenario. The proposed modified UNet framework achieves notable accuracy, with a Jaccard Index and BF Score of 0.95, 0.92, and 0.73, respectively. Subsequently, our proposed framework’s outcomes are visually analyzed using Explainable Artificial Intelligence (XAI) algorithms. It showcases the proposed framework’s ability to accurately segment lesions even in the presence of various artifacts such as hair, clinical swatches, markers, and variations in intensity and size. The performance of the proposed framework is compared with the existing works. The efficacy and robustness of the proposed framework are evident from the
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