cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5725648
title: PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting

description: Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has great potential in increasing the flexibility and reducing the computational cost of seismic modeling and inversion. We propose a novel implementation of PINN using frequency upscaling and neuron splitting, which allows the neural network model to grow in size as we increase the frequency while leveraging the information from the pre-trained model for lower-frequency wavefields, resulting in fast convergence to high-accuracy wavefield solutions. In this letter, we present the relevant dataset to the paper.
DOI: 10.5519/qd.34kfv9gc
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (413 records)
DOI: 10.6084/m9.figshare.20059091
title: Bats in Temples of south India

description: The data includes microhabitats inside temples where bats were roosting. Dark rooms: closed rooms with few small openings, low natural light and not frequently used by people; Crevices: stone pillars that supported the roof created gaps between the joints for some species to roost; Temple towers: conical structures built over the entrance or the deity and Walkways: wide walking space around the deity room covered by a stone roof. We also counted the number of independent buildings in the temple complex, which bats use to move between them during the day due to disturbance. Temple size was based on visual observation; those with deity rooms and walkways were considered small, those with deity rooms, walkways and towers considered medium-sized, and those having deity rooms, multiple towers, and two walkways (inside and outside) were considered large. We also recorded the number of trees in the temple complex and open wells used by bats. <br> We classified disturbances such as renovation, construction, power washing, and other maintenance activities as 0 or 1. A temple received a value of 0 when there was no disturbance and a value of 1 when there was a disturbance. We also collected information on the number of temple visitors and classified temples on a scale of 1 to 3, with 1 indicating few visitors and 3 indicating having large crowds. <br> <strong>Land-use features</strong> We used the composite analysis of land cover available using high-resolution (10m) European Space Agency (ESRI) data for 2020 [41] to identify major land uses around temples in the region.
DOI: 10.34740/kaggle/dsv/3792776
title: AutoXGB
DOI: 10.34740/kaggle/ds/2211410
title: Nigerian House Prices
DOI: 10.18112/openneuro.ds004148.v1.0.0
title: A test-retest resting and cognitive state EEG dataset
DOI: 10.5281/zenodo.6621996
title: Dataset relate to article: "Alteration of the immunophenotype and cytokine profiles in patients affected by neuroendocrine neoplasms"

description: This record contains data related to article "<strong>Alteration of the immunophenotype and cytokine profiles in patients affected by neuroendocrine neoplasms"</strong> <strong>Abstract:</strong> Neuroendocrine neoplasms (NENs) are tumors that arise from cells of the endocrine system and are most common in the gastrointestinal tract, the pancreas, and the lungs. Their incidence is rapidly increasing and the therapeutic options available are limited, as NENs frequently show resistance to pharmacological therapy. Since the immune system can interfere with tumor growth and response to therapy, using flow cytometry we investigated the immunophenotype in samples of peripheral blood leukocytes from patients with pancreatic (Pan-NEN, n = 45) and pulmonary (Lung-NEN, n = 19) neuroendocrine neoplasm and from 17 healthy donors. Moreover, we performed a multiplex analysis of 13 key cytokines and growth factors essential for the immune response in the plasma of NEN patients and controls. Patients presented with a higher percentage of granulocytes, a lower percentage of lymphocytes and an increase in the granulocytes to lymphocytes ratio (GLR) compared to healthy donors. These alterations were more marked in patients presenting metastasis. Since somatostatin analogs (SSAs) represented the main treatment modality in our cohort of patients, we analyzed its impact on the immunophenotype. SSAs restored the immunophenotype of patients to that seen in healthy donors. Finally, Pan-NEN patients showed a higher plasma concentration of IP-10, MCP-1, and IL-8 compared to healthy donors, suggesting a potential role for these cytokines as diagnostic biomarkers. In conclusion, this study demonstrates profound differences in the immunophenotype of patients with Pan- and Lung-NENs compared to healthy individuals; these alterations were partially restored by therapy.
DOI: 10.5281/zenodo.6621995
title: Dataset relate to article: "Alteration of the immunophenotype and cytokine profiles in patients affected by neuroendocrine neoplasms"

description: This record contains data related to article "<strong>Alteration of the immunophenotype and cytokine profiles in patients affected by neuroendocrine neoplasms"</strong> <strong>Abstract:</strong> Neuroendocrine neoplasms (NENs) are tumors that arise from cells of the endocrine system and are most common in the gastrointestinal tract, the pancreas, and the lungs. Their incidence is rapidly increasing and the therapeutic options available are limited, as NENs frequently show resistance to pharmacological therapy. Since the immune system can interfere with tumor growth and response to therapy, using flow cytometry we investigated the immunophenotype in samples of peripheral blood leukocytes from patients with pancreatic (Pan-NEN, n = 45) and pulmonary (Lung-NEN, n = 19) neuroendocrine neoplasm and from 17 healthy donors. Moreover, we performed a multiplex analysis of 13 key cytokines and growth factors essential for the immune response in the plasma of NEN patients and controls. Patients presented with a higher percentage of granulocytes, a lower percentage of lymphocytes and an increase in the granulocytes to lymphocytes ratio (GLR) compared to healthy donors. These alterations were more marked in patients presenting metastasis. Since somatostatin analogs (SSAs) represented the main treatment modality in our cohort of patients, we analyzed its impact on the immunophenotype. SSAs restored the immunophenotype of patients to that seen in healthy donors. Finally, Pan-NEN patients showed a higher plasma concentration of IP-10, MCP-1, and IL-8 compared to healthy donors, suggesting a potential role for these cytokines as diagnostic biomarkers. In conclusion, this study demonstrates profound differences in the immunophenotype of patients with Pan- and Lung-NENs compared to healthy individuals; these alterations were partially restored by therapy.
DOI: 10.34740/kaggle/dsv/3792773
title: LUFlow Network Intrusion Detection Data Set
DOI: 10.5281/zenodo.6637507
title: Associations between Dictyocaulus viviparus bulk tank milk seropositivity and meteorological variables

description: This dataset belongs to the scientific article with the same title "Associations between <em>Dictyocaulus viviparus</em> bulk tank milk seropositivity and meteorological variables" to be published in International Journal for Parasitology.
DOI: 10.5281/zenodo.6637506
title: Associations between Dictyocaulus viviparus bulk tank milk seropositivity and meteorological variables

description: This dataset belongs to the scientific article with the same title "Associations between <em>Dictyocaulus viviparus</em> bulk tank milk seropositivity and meteorological variables" to be published in International Journal for Parasitology.
DOI: 10.6084/m9.figshare.20059301
title: MetaNeighbor- BICCN workshop

description: Data for the MetaNeighbor notebook presented at the BICCN workshop (22/06/15).
DOI: 10.6084/m9.figshare.20059301.v1
title: MetaNeighbor- BICCN workshop

description: Data for the MetaNeighbor notebook presented at the BICCN workshop (22/06/15).
DOI: 10.57770/wdjde6
title: Stock Selection Recommender

description: Stock Selection Recommender
DOI: 10.57770/rzl3hk
title: iRDPG Quan trading

description: iRDPG Quan trading
DOI: 10.57770/ooeucf
title: DRL Portifolio Management

description: DRL Portifolio Management
DOI: 10.6084/m9.figshare.20055221
title: ozone

description: ozone figure data
DOI: 10.6084/m9.figshare.20055221.v1
title: ozone

description: ozone figure data
DOI: 10.57770/emsofj
title: Replication Data for: Bridging The Visual Gap: Wide-Range Image Blending

description: In this paper we propose a new problem scenario in image processing, wide-range image blending, which aims to smoothly merge two different input photos into a panorama by generating novel image content for the intermediate region between them. Although such problem is closely related to the topics of image inpainting, image outpainting, and image blending, none of the approaches from these topics is able to easily address it. We introduce an effective deep-learning model to realize wide-range image blending, where a novel Bidirectional Content Transfer module is proposed to perform the conditional prediction for the feature representation of the intermediate region via recurrent neural networks. In addition to ensuring the spatial and semantic consistency during the blending, we also adopt the contextual attention mechanism as well as the adversarial learning scheme in our proposed method for improving the visual quality of the resultant panorama. We experimentally demonstrate that our proposed method is not only able to produce visually appealing results for wide-range image blending, but also able to provide superior performance with respect to several baselines built upon the state-of-the-art image inpainting and outpainting approaches.
DOI: 10.57770/p7tnvx
title: Replication Data for: Learning Facial Representations from the Cycle-consistency of Face

description: Faces manifest large variations in many aspects, such as identity, expression, pose, and face styling. Therefore, it is a great challenge to disentangle and extract these characteristics from facial images, especially in an unsupervised manner. In this work, we introduce cycle-consistency in facial characteristics as free supervisory signal to learn facial representations from unlabeled facial images. The learning is realized by superimposing the facial motion cycle-consistency and identity cycle-consistency constraints. The main idea of the facial motion cycle-consistency is that, given a face with expression, we can perform de-expression to a neutral face via the removal of facial motion and further perform re-expression to reconstruct back to the original face. The main idea of the identity cycle-consistency is to exploit both de-identity into mean face by depriving the given neutral face of its identity via feature re-normalization and re-identity into neutral face by adding the personal attributes to the mean face. At training time, our model learns to disentangle two distinct facial representations to be useful for performing cycle-consistent face reconstruction. At test time, we use the linear protocol scheme for evaluating facial representations on various tasks, including facial expression recognition and head pose regression. We also can directly apply the learnt facial representations to person recognition, frontalization and image-to-image translation. Our experiments show that the results of our approach is competitive with those of existing methods, demonstrating the rich and unique information embedded in the disentangled representations.
DOI: 10.57770/arfqtp
title: Replication Data for: Stylizing 3D Scene via Implicit Representation and HyperNetwork

description: In this work, we aim to address the 3D scene stylization problem - generating stylized images of the scene at arbitrary novel view angles. A straightforward solution is to combine existing novel view synthesis and image/video style transfer approaches, which often leads to blurry results or inconsistent appearance. Inspired by the high quality results of the neural radiance fields (NeRF) method, we propose a joint framework to directly render novel views with the desired style. Our framework consists of two components: an implicit representation of the 3D scene with the neural radiance field model, and a hypernetwork to transfer the style information into the scene representation. In particular, our implicit representation model disentangles the scene into the geometry and appearance branches, and the hypernetwork learns to predict the parameters of the appearance branch from the reference style image. To alleviate the training difficulties and memory burden, we propose a two-stage training procedure and a patch sub-sampling approach to optimize the style and content losses with the neural radiance field model. After optimization, our model is able to render consistent novel views at arbitrary view angles with arbitrary style. Both quantitative evaluation and human subject study have demonstrated that the proposed method generates faithful stylization results with consistent appearance across different views.
DOI: 10.57770/tcuw9m
title: Replication Data for: Dual-Stream Fusion Network for Spatiotemporal Video Super-Resolution

description: Visual data upsampling has been an important research topic for improving the perceptual quality and benefiting various computer vision applications. In recent years, we have witnessed remarkable progresses brought by the re-naissance of deep learning techniques for video or image super-resolution. However, most existing methods focus on advancing super-resolution at either spatial or temporal direction, i.e, to increase the spatial resolution or the video frame rate. In this paper, we instead turn to discuss both directions jointly and tackle the spatiotemporal upsampling problem. Our method is based on an important observation that: even the direct cascade of prior research in spatial and temporal super-resolution can achieve the spatiotemporal upsampling, changing orders for combining them would lead to results with a complementary property. Thus, we propose a dual-stream fusion network to adaptively fuse the intermediate results produced by two spatiotemporal up-sampling streams, where the first stream applies the spatial super-resolution followed by the temporal super-resolution, while the second one is with the reverse order of cascade. Extensive experiments verify the efficacy of the proposed method against several baselines. Moreover, we investigate various spatial and temporal upsampling methods as the basis in our two-stream model and demonstrate the flexibility with wide applicability of the proposed framework.
DOI: 10.57770/lszctr
title: Replication Data for: Single Image Reflection Removal with Edge Guidance, Reflection Classifier, and Recurrent Decomposition

description: Removing undesired reflection from an image captured through a glass window is a notable task in computer vision. In this paper, we propose a novel model with auxiliary techniques to tackle the problem of single image reflection removal. Our model takes a reflection contaminated image as input, and decomposes it into the reflection layer and the transmission layer. In order to ensure quality of the transmission layer, we introduce three auxiliary techniques into our architecture, including the edge guidance, a reflection classifier, and the recurrent decomposition. The contributions and the efficacy of these techniques are investigated and verified in the ablation study. Furthermore, in comparison to the state-of-the-art baselines of reflection removal, both quantitative and qualitative results demonstrate that our proposed method is able to deal with different kinds of images, achieving the best results in average.
DOI: 10.6084/m9.figshare.20059295
title: 10X_genomics_data_2

description: This dataset includes 11 h5ad files processed from raw data in the website of 10X datasets https://www.10xgenomics.com/resources/datasets <br>
DOI: 10.6084/m9.figshare.20059295.v1
title: 10X_genomics_data_2

description: This dataset includes 11 h5ad files processed from raw data in the website of 10X datasets https://www.10xgenomics.com/resources/datasets <br>
DOI: 10.57770/zf7yu4
title: Replication Data for: Class-incremental Learning with Rectified Feature-Graph Preservation

description: In this paper, we address the problem of distillation-based class-incremental learning with a single head. A central theme of this task is to learn new classes that arrive in sequential phases over time while keeping the model's capability of recognizing seen classes with only limited memory for preserving seen data samples. Many regularization strategies have been proposed to mitigate the phenomenon of catastrophic forgetting. To understand better the essence of these regularizations, we introduce a feature-graph preservation perspective. Insights into their merits and faults motivate our weighted-Euclidean regularization for old knowledge preservation. We further propose rectified cosine normalization and show how it can work with binary cross-entropy to increase class separation for effective learning of new classes. Experimental results on both CIFAR-100 and ImageNet datasets demonstrate that our method outperforms the state-of-the-art approaches in reducing classification error, easing catastrophic forgetting, and encouraging evenly balanced accuracy over different classes.
DOI: 10.5281/zenodo.5747173
title: Sensor Position Comparison Dataset

description: <pre> </pre> A dataset containing IMU recordings with full motion capture reference from 14 participants (approx. 10000 strides). Each participant was equipped with 15 synchronised IMUs (6 at different positions at each shoe, 1 at each ankle, and 1 and the lower back). The main goal of the dataset is to compare the recorded signals of the 6 sensors attached to each foot. For more information about the dataset check the `README.md` file in the dataset. If you are using the dataset, please cite the following paper: Küderle, Arne, Nils Roth, Jovana Zlatanovic, Markus Zrenner, Bjoern Eskofier, and Felix Kluge.<br> “The Placement of Foot-Mounted IMU Sensors Does Affect the Accuracy of Spatial Parameters during Regular Walking.”<br> PLOS ONE 17, no. 6 (June 9, 2022): e0269567. https://doi.org/10.1371/journal.pone.0269567.
DOI: 10.5281/zenodo.5747172
title: Sensor Position Comparison Dataset

description: <pre> </pre> A dataset containing IMU recordings with full motion capture reference from 14 participants (approx. 10000 strides). Each participant was equipped with 15 synchronised IMUs (6 at different positions at each shoe, 1 at each ankle, and 1 and the lower back). The main goal of the dataset is to compare the recorded signals of the 6 sensors attached to each foot. For more information about the dataset check the `README.md` file in the dataset. If you are using the dataset, please cite the following paper: Küderle, Arne, Nils Roth, Jovana Zlatanovic, Markus Zrenner, Bjoern Eskofier, and Felix Kluge.<br> “The Placement of Foot-Mounted IMU Sensors Does Affect the Accuracy of Spatial Parameters during Regular Walking.”<br> PLOS ONE 17, no. 6 (June 9, 2022): e0269567. https://doi.org/10.1371/journal.pone.0269567.
DOI: 10.5281/zenodo.6637521
title: Simulation data for "Nonlinear electron phase-space dynamics in spontaneous excitation of falling-tone chorus" submitting to Geophysical Research Letters

description: Simulation data for "Nonlinear electron phase-space dynamics in spontaneous excitation of falling-tone chorus" submitting to Geophysical Research Letters. Including the simulation input parameter file and the necessary output data for analysis described in the article. The output data consists of waveform data, wave intensity profile, binned phase space distribution, etc. A detailed guide to load the output data is included in the zipped file as well.
DOI: 10.5281/zenodo.6637520
title: Simulation data for "Nonlinear electron phase-space dynamics in spontaneous excitation of falling-tone chorus" submitting to Geophysical Research Letters

description: Simulation data for "Nonlinear electron phase-space dynamics in spontaneous excitation of falling-tone chorus" submitting to Geophysical Research Letters. Including the simulation input parameter file and the necessary output data for analysis described in the article. The output data consists of waveform data, wave intensity profile, binned phase space distribution, etc. A detailed guide to load the output data is included in the zipped file as well.
DOI: 10.7910/dvn/wybqtm
title: Sense of belonging in new work setting - a qualitative study.

description: Using a qualitative, empirical study of continuing hybrid, if not complete, remote work, we presented certain levels and factors that can positively influence the sense of belonging despite the geographical distance. The results showed the level on which the different factors influencing the sense of belonging are found. On the level of "little influence" was the factor "job content/design", on the further inner level "significant influence" were the factors "leadership, relationship, team bonding, and trust" and in the core was the "great influence" level with "feel valued". In conclusion, organizations can support the development of a sense of belonging by consciously taking these factors and levels into account e.g., by encouraging the development of relationships within the team and its leadership. Similarly, ensuring that employees can work from home and contribute in a meaningful way, so that employees feel included and valued, and can voice their concerns and weaknesses. The lack of belonging can also be mitigated by having a manager or an external person in the workplace to guide and coach employees.
DOI: 10.57770/f4daqz
title: Replication Data for: Boosting Image and Video Compression via Learning Latent Residual Patterns

description: Reducing compression artifacts is essential for streaming videos with a better quality under a limited bandwidth. To tackle this problem, existing methods aim to directly recover details from the compressed video but do not consider learning rich information in uncompressed videos to aid this process. In this paper, we focus on utilizing the residual information, which is the difference between a compressed video and its corresponding original/uncompressed one, and propose a fairly efficient way to transmit the residual with the compressed video in order to boost the quality of video compression. Our proposed method is realized by learning to discover the patterns in the residual and storing them offline as dictionary-like patterns. During the testing stage, e.g., for video streaming, the residual is transmitted in the form of pattern indexes to reduce the cost of communication, and thus the original residual information can be easily retrieved back from the dictionary of learned residual patterns. We show the effectiveness of our framework on numerous datasets under various video compression coding methods. In addition, the proposed pipeline can be widely applied to the image compression task and reduce artifacts produced from conventional and CNN-based compression algorithms
DOI: 10.34740/kaggle/dsv/3792922
title: WallStreetBets 2022
DOI: 10.5281/zenodo.6637526
title: An integrated dataset of daily lake surface water temperature over Tibetan Plateau

description: A dataset for daily surface temperature of 160 lakes over Tibetan Plateau for period from 1978 to 2017. The new dataset was developed based on combination of remote sensing (MODIS) and model (slightly modified <em>air2water </em>model).
DOI: 10.5281/zenodo.5878435
title: An integrated dataset of daily lake surface water temperature over Tibetan Plateau

description: A dataset for daily surface temperature of 160 lakes over Tibetan Plateau for period from 1978 to 2017. The new dataset was developed based on combination of remote sensing (MODIS) and model (slightly modified <em>air2water </em>model).