cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30325504.v4
title: University of Nigeria, Nsukka (UNN) 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department

description: University of Nigeria, Nsukka (UNN) 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.6084/m9.figshare.30325504.v5
title: University of Delta, Agbor 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department

description: University of Delta, Agbor 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.21227/cawa-m678
title: "Deep Balanced Prior for Refined Self-supervised Reconstruction of Snapshot Spectral Compressive Imaging"

description: "Hyperspectral imaging is an advanced instrumentation technology that can obtain the spectral features through measurement. Typically, hyperspectral images (HSIs) contain more spectral bands than standard RGB images, enabling them to store more information and reveal more detailed characteristics of the scene. Relying on this property, HSIs have been widely applied to many computer vision related tasks, agriculture, remote sensing, medicine, etc. Traditional imaging systems equipped with spectrometers scan scenes either spatially or spectrally to acquire HSIs, a process that typically demands considerable time. Consequently, these conventional systems are not ideal for capturing and measuring dynamic scenes. In recent developments, researchers have turned to snapshot compressive imaging (SCI) systems for obtaining HSIs. These SCI systems compress information of snapshots along the spectral dimension into one single 2D measurement. Nowadays, the development of SCI systems has become increasingly diverse . Among these, coded aperture snapshot spectral imaging (CASSI), which uses a physical mask and a prism to implement the multiplexing modulation, has been widely applied as a mature forward compression system."
DOI: 10.6084/m9.figshare.30325504.v6
title: University of Port-Harcourt (UNIPORT) 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department

description: University of Port-Harcourt (UNIPORT) 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.6084/m9.figshare.30325504.v7
title: University of Uyo (UNIUYO) 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department

description: University of Uyo (UNIUYO) 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.21979/n9/dmb2qk
title: Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration

description: Although learning-based image restoration methods have made significant progress, they still struggle with limited generalization to real-world scenarios due to the substantial domain gap caused by training on synthetic data. Existing methods address this issue by improving data synthesis pipelines, estimating degradation kernels, employing deep internal learning, and performing domain adaptation and regularization. Previous domain adaptation methods have sought to bridge the domain gap by learning domain-invariant knowledge in either feature or pixel space. However, these techniques often struggle to extend to low-level vision tasks within a stable and compact framework. In this paper, we show that it is possible to perform domain adaptation via the noise space using diffusion models. In particular, by leveraging the unique property of how auxiliary conditional inputs influence the multi-step denoising process, we derive a meaningful diffusion loss that guides the restoration model in progressively aligning both restored synthetic and real-world outputs with a target clean distribution. We refer to this method as denoising as adaptation. To prevent shortcuts during joint training, we present crucial strategies such as channel-shuffling layer and residual-swapping contrastive learning in the diffusion model. They implicitly blur the boundaries between conditioned synthetic and real data and prevent the reliance of the model on easily distinguishable features. Experimental results on three classical image restoration tasks, namely denoising, deblurring, and deraining, demonstrate the effectiveness of the proposed method.
DOI: 10.6084/m9.figshare.30325504.v8
title: Federal University of Technology, Ikot Abasi 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department

description: Federal University of Technology, Ikot Abasi 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.21979/n9/jdzoje
title: SurMo: Surface-based 4D Motion Modeling for Dynamic Human Rendering (CVPR 2024)

description: Dynamic human rendering from video sequences has achieved remarkable progress by formulating the rendering as a mapping from static poses to human images. However, existing methods focus on the human appearance reconstruction of every single frame while the temporal motion relations are not fully explored. In this paper, we propose a new 4D motion modeling paradigm, SurMo, that jointly models the temporal dynamics and human appearances in a unified framework with three key designs: 1) Surface-based motion encoding that models 4D human motions with an efficient compact surface-based triplane. It encodes both spatial and temporal motion relations on the dense surface manifold of a statistical body template, which inherits body topology priors for generalizable novel view synthesis with sparse training observations. 2) Physical motion decoding that is designed to encourage physical motion learning by decoding the motion triplane features at timestep t to predict both spatial derivatives and temporal derivatives at the next timestep t+1 in the training stage. 3) 4D appearance decoding that renders the motion triplanes into images by an efficient volumetric surface-conditioned renderer that focuses on the rendering of body surfaces with motion learning conditioning. Extensive experiments validate the state-of-the-art performance of our new paradigm and illustrate the expressiveness of surface-based motion triplanes for rendering high-fidelity view-consistent humans with fast motions and even motion-dependent shadows.
DOI: 10.6084/m9.figshare.30325504.v9
title: Federal University of Health Sciences, Azare 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department

description: Federal University of Health Sciences, Azare 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.5281/zenodo.17309328
title: The benchmark instances of the DRFJSP-MO (in MATLAB)

description: 30 benchmark instances (ZLG01 ~ ZLG30) for the dual-resource flexible job shop scheduling problem with multiple outsourcing centers (DRFJSP-MO) is provided in this file.

DRFJSP-MO can also be defined by other names. As long as your research conforms to the problem definition, this test suite can be used.

The ZLG test suite contains problems with different parameters, increasing the number of jobs from six to 30, the number of machines from five to 12, the number of outsourcing workshops from one to four, and the number of workers in the internal workshop from three to seven.

It is worth noting that this is a version in MATLAB. You can use the "load" command to read it. For example: load('ZLG01.mat')

---------------------------------------------------------------The explanations for the main variable names are as follows:1. JobNum: The number of jobs.2. InMachNum: The number of machines in the internal workshop.3. OutShopNum: The number of the outsourcing centers (workshops)4. OutShopMach: The number of machines in each outsourcing center. It is a vector.5. MachineNum: The total number of machines.6. WorkerNum: The total number of workers.7. OperaVec: Operation number vector, displaying the number of operations for each job in sequence.8. OperaMax: The maximum number of operations for all jobs.9. OperaNum: The total number of operations for all jobs.10. Periods: Time window number vector, showing the number of service time windows for each outsourcing machine in sequence.11. JobInfo: The processing information of the job (it is a structure) which includes the optional machine set for each operation and the corresponding processing time.12. WorkerInfo: Worker information (it is a structure), showing the set of controllable machines for each worker.13. ServiceWindow: Time window information (it is a structure), which includes the start and end time for each time window.14. ReleTime: The release time of each job.15. DeliTime: The delivery time for each job.16. ShopTransfer: The transfer time matrix between different workshops.17. PerShopMach: The number of machines each workshop possesses.
DOI: 10.5281/zenodo.17309329
title: The benchmark instances of the DRFJSP-MO (in MATLAB)

description: 30 benchmark instances (ZLG01 ~ ZLG30) for the dual-resource flexible job shop scheduling problem with multiple outsourcing centers (DRFJSP-MO) is provided in this file.

DRFJSP-MO can also be defined by other names. As long as your research conforms to the problem definition, this test suite can be used.

The ZLG test suite contains problems with different parameters, increasing the number of jobs from six to 30, the number of machines from five to 12, the number of outsourcing workshops from one to four, and the number of workers in the internal workshop from three to seven.

It is worth noting that this is a version in MATLAB. You can use the "load" command to read it. For example: load('ZLG01.mat')

---------------------------------------------------------------The explanations for the main variable names are as follows:1. JobNum: The number of jobs.2. InMachNum: The number of machines in the internal workshop.3. OutShopNum: The number of the outsourcing centers (workshops)4. OutShopMach: The number of machines in each outsourcing center. It is a vector.5. MachineNum: The total number of machines.6. WorkerNum: The total number of workers.7. OperaVec: Operation number vector, displaying the number of operations for each job in sequence.8. OperaMax: The maximum number of operations for all jobs.9. OperaNum: The total number of operations for all jobs.10. Periods: Time window number vector, showing the number of service time windows for each outsourcing machine in sequence.11. JobInfo: The processing information of the job (it is a structure) which includes the optional machine set for each operation and the corresponding processing time.12. WorkerInfo: Worker information (it is a structure), showing the set of controllable machines for each worker.13. ServiceWindow: Time window information (it is a structure), which includes the start and end time for each time window.14. ReleTime: The release time of each job.15. DeliTime: The delivery time for each job.16. ShopTransfer: The transfer time matrix between different workshops.17. PerShopMach: The number of machines each workshop possesses.
DOI: 10.6084/m9.figshare.30325504.v10
title: Federal University of Health Sciences, Ila Orangun 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department

description: Federal University of Health Sciences, Ila Orangun 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.6084/m9.figshare.30325504.v11
title: FUHSI 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department

description: FUHSI 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.6084/m9.figshare.30325504.v12
title: Nigerian Maritime University Okerenkoko 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department

description: Nigerian Maritime University Okerenkoko 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.6084/m9.figshare.30325504.v13
title: Air Force Institute of Technology, Kaduna 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department

description: Air Force Institute of Technology, Kaduna 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.6084/m9.figshare.30325504.v14
title: University of Health Technology, Otukpo 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department

description: University of Health Technology, Otukpo 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.21979/n9/wrpwan
title: FashionEngine: Interactive 3D Human Generation and Editing via Multimodal Controls

description: We present FashionEngine, an interactive 3D human generation and editing system that creates 3D digital humans via user-friendly multimodal controls such as natural languages, visual perceptions, and hand-drawing sketches. FashionEngine automates the 3D human production with three key components: 1) A pre-trained 3D human diffusion model that learns to model 3D humans in a semantic UV latent space from 2D image training data, which provides strong priors for diverse generation and editing tasks. 2) Multimodality-UV Space encoding the texture appearance, shape topology, and textual semantics of human clothing in a canonical UV-aligned space, which faithfully aligns the user multimodal inputs with the implicit UV latent space for controllable 3D human editing. The multimodality-UV space is shared across different user inputs, such as texts, images, and sketches, which enables various joint multimodal editing tasks. 3) Multimodality-UV Aligned Sampler learns to sample high-quality and diverse 3D humans from the diffusion prior. Extensive experiments validate FashionEngine's state-of-the-art performance for conditional generation/editing tasks. In addition, we present an interactive user interface for our FashionEngine that enables both conditional and unconditional generation tasks, and editing tasks including pose/view/shape control, text-, image-, and sketch-driven 3D human editing and 3D virtual try-on, in a unified framework.
DOI: 10.6084/m9.figshare.30325504.v15
title: David Nweze Umahi Federal University of Medical Sciences, Uburu 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department

description: David Nweze Umahi Federal University of Medical Sciences, Uburu 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.21979/n9/v1xdvb
title: TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment

description: Multivariate time series forecasting (MTSF) aims to learn temporal dynamics among variables to forecast future time series. Existing statistical and deep learning-based methods suffer from limited learnable parameters and small-scale training data. Recently, large language models (LLMs) combining time series with textual prompts have achieved promising performance in MTSF. However, we discovered that current LLM-based solutions fall short in learning disentangled embeddings. We introduce TimeCMA, an intuitive yet effective framework for MTSF via cross-modality alignment. Specifically, we present a dual-modality encoding with two branches: the time series encoding branch extracts disentangled yet weak time series embeddings, and the LLM-empowered encoding branch wraps the same time series with text as prompts to obtain entangled yet robust prompt embeddings. As a result, such a cross-modality alignment retrieves both disentangled and robust time series embeddings, ``the best of two worlds'', from the prompt embeddings based on time series and prompt modality similarities. As another key design, to reduce the computational costs from time series with their length textual prompts, we design an effective prompt to encourage the most essential temporal information to be encapsulated in the last token: only the last token is passed to downstream prediction. We further store the last token embeddings to accelerate inference speed. Extensive experiments on eight real datasets demonstrate that TimeCMA outperforms state-of-the-arts.
DOI: 10.6084/m9.figshare.30325504.v16
title: Adeyemi University of Education, Ondo 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department

description: Adeyemi University of Education, Ondo 2025/2026 Diploma Form/Pre Degree Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department +2347076040542 now to apply and for mode of making school fees, Tutions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.21985/n2-z5g0-zd80
title: Reproducibility_Packet_v4