In this paper, we propose to use weighted (Formula presented.) -norm for approximating the solution of general (Formula presented.) -norm regularization problem for recovering hyperspectral images (HSI) corrupted by a...
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Mobile edge computing (MEC) significantly boosts the computing power and reduces the energy consumption of Internet of Things (IoT) devices, serving as a valuable complement to cloud computing. The application of unma...
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Digit-serial arithmetic has emerged as a viable approach for designing hardware accelerators, reducing interconnections, area utilization, and power consumption. However, conventional methods suffer from performance a...
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Introduction: Depression is a complex mental health condition, particularly prevalent among young people aged 10–24, a group experiencing a sharp rise in cases. Traditional screening methods, such as the PHQ-9, are p...
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Introduction: Depression is a complex mental health condition, particularly prevalent among young people aged 10–24, a group experiencing a sharp rise in cases. Traditional screening methods, such as the PHQ-9, are particularly challenging for children in pediatric primary care due to practical limitations. AI has potential to help but the scarcity of annotated datasets in mental health, combined with the computational costs of training, highlights the need for efficient, zero-shot approaches. Large Language Models (LLMs) offer a promising, computationally affordable zero-shot solution by extracting relevant text segments from electronic patient notes to support clinicians in identifying depressive symptoms, as well as for downstream AI. In this work, we investigate the feasibility of state-of-the-art LLMs for depressive symptom extraction in pediatric settings (ages 6–24). This approach aims to complement traditional screening and minimize diagnostic errors. Methods: We examined free text of the EHRs of pediatric patients with the diagnosis of depression or related mood disorders (age groups 6-24, 1.8K patients) from at the Cincinnati Children’s Hospital Medical Center. We noticed drastic inconsistencies in the application and documentation of PHQ-9 screening highlighting the difficulty in obtaining comprehensive diagnostic data in these conditions. We manually annotated notes for 22 patients with 16 depression-related symptom categories. We leveraged the combination of Beck’s Depression Inventory (BDI) and the PHQ-9 to develop tailored categories specifically suited for pediatric depression symptoms. We then applied three state-of-the-art Large Language Models (LLMs) (FLAN T5, Llama and Phi) to automate the identification of these symptom categories. Results: Our findings show that all LLMs are 60% more efficient than word match with Flan leading in precision (average F1: 0.65, precision: 0.78), excelling in the extraction of more rare symptoms like "sleep problem
There are several situations where it would be convenient if a quantity of interest essential to support a medical or regulatory decision could be predicted as a function of other measurable quantities rather than mea...
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This paper studies and presents data-driven methods for finding rankings of traffic links in a network for optimal traffic data reconstruction based on measurements taken from a subset of links. The link ranking repre...
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This paper studies and presents data-driven methods for finding rankings of traffic links in a network for optimal traffic data reconstruction based on measurements taken from a subset of links. The link ranking represents the importance of respective links in terms of reconstructing traffic information from sparsely placed sensors, connected vehicles, or other state-of-the-art methods. We first present a baseline method based matrix factorization of the eigen-vector basis matrix, followed by column pivoting. Moreover, we propose a reinforcement learning framework to improve the ranking method when the traffic data is used for the purpose of routing. This study utilizes dynamic traffic data that is observed and estimated from simulation.
The heterogeneity inherent in tau positron emission tomog-raphy (PET) imaging data across different tracers challenges the integration of multi-site tau PET data, thereby necessitating the trustful harmonization techn...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
The heterogeneity inherent in tau positron emission tomog-raphy (PET) imaging data across different tracers challenges the integration of multi-site tau PET data, thereby necessitating the trustful harmonization technique for a better utilization of the emerging large-scale datasets. Unlike other imaging modalities, the harmonization among multi-site tau PET data involves more than intensity mapping but contains in-tricate pattern alterations attributed to tracer binding proper-ties, which makes the existing statistical methods inadequate. Meanwhile, the effective data preprocessing is required to eliminate the artifacts caused by off-target binding and partial volume effect for meaningful comparison and harmonization. In this paper, we propose a systematic tau PET harmonization framework that involves the surface-based data preprocessing and diffusion model for generating the vertex-wise mapping between multi-site tau standardized uptake value ra-tio (SUVR) on the cortical surface. In the experiments, using large-scale Alzheimer's Disease Neuroimaging Initiative (ADNI) and Health and Aging Brain Study-Health Dispari-ties (HABS-HD) data with different tracers, we demonstrate our method can successfully achieve harmonization by gen-erating the SUVR maps with consistent pattern distributions and persevering the individual variability.
3D Gaussian Splatting has shown remarkable capabilities in novel view rendering tasks and exhibits significant potential for multi-view optimization. However, the original 3D Gaussian Splatting lacks color representat...
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Personalized recommendation is of paramount importance in online content platforms like Kuai and Tencent. To ensure accurate recommendations, it is crucial to consider multi-modal information in both items and user-us...
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Personalized recommendation is of paramount importance in online content platforms like Kuai and Tencent. To ensure accurate recommendations, it is crucial to consider multi-modal information in both items and user-user/item interactions. While existing works on multimedia recommendation have made strides in leveraging multi-modal contents to enrich item representations, many of them overlook the practical scenario of multiple modality missing. As a result, the performance of recommendation systems can be significantly compromised in such cases. In this paper, we introduce a novel multi-modal adversarial method called MMAM, which aims to provide reliable personalized recommendation services even in the presence of uncertain missing modalities. The core idea behind MMAM is to design a generator that can effectively encode both user-user/item interactions and multi-modal contents, taking into account various missing cases. The generator is trained to learn transferable features from different combinations of missing modalities in order to deceive a discriminative classifier. Additionally, we propose a modal discriminator that can classify the missing cases of multi-modalities, further enhancing the capability of the model. Moreover, a well-equipped predictor utilizes the transferable features to predict potential user interests. To improve the prediction accuracy, we design a type discriminator that enhances the classification of link types. By employing a mini-max game between the generator and the discriminators, MMAM successfully obtains transferable features that encompass multi-modal contents, even when facing uncertain missing modalities. We conduct extensive experiments on industrial datasets, including Kuai and Tencent. Comparing with state-of-the-art approaches, MMAM achieves improvements in personalized recommendation tasks under uncertain missing modalities. MMAM holds promise for enhancing multi-modal personalized recommendations in real-world applications
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