ZIP

Global Illumination (GI) is a strategy in computer graphics to add a certain degree of realism.  Several approaches exist to achieve such a visual effect for computer-generated imagery. The most physically accurate approach is through conventional raytracing. It produces similar realistic results by trading-off time and computational-resource intensive, making them unsuitable for real-time usage. For more real-time usage scenarios, a set of faster algorithms exists that utilize post-processing on top of rasterization rather than performing ray-tracing.

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Objective: Stereoelectroencephalography (SEEG) is an established invasive diagnostic technique for use in patients with drug-resistant focal epilepsy evaluated before resective epilepsy surgery. The factors that influence the accuracy of electrode implantation are not fully understood. Adequate accuracy prevents the risk of major surgery complications. Precise knowledge of the anatomical positions of individual electrode contacts is crucial for the interpretation of SEEG recordings and subsequent surgery.

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276 Views

To promote intelligent water services and accelerate the water industry's modernization process, accurately predicting regional residents' water demand and reducing energy consumption for secondary water supply is a major challenge for scientific scheduling and efficient management of urban water supply. This paper proposes a deep learning-based approach for demand forecasting in residential communities. The approach first identifies and corrects outliers in raw water supply data, and incorporates additional features such as epidemics and meteorological information.

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397 Views

Coordinates in the Standard *.dat Format:

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The dataset contains detailed information on a 100kVA transformer in ONAN cooling mode. The file consists of three PDF documents, which describe the transformer’s overall structure, core structure and size, winding structure and dimension, respectively. 

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The RAHG experimental data includes six public datasets and one self-built dataset. The experimental process of RAHG on these seven datasets is also recorded in it

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The RAHG experimental data includes four public datasets and one self-built dataset. The experimental process of RAHG on these five datasets is also recorded.

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  This dataset is comprised of two parts. In dataset 1, we provide Buddha images, along with positions of hands and faces. Dataset 2 provides Buddha images only and can be used for Buddha statue classification.

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This dataset contains the raw data of the measurements/simulations presented in "Modulation Scheme Analysis for Low-Power Leadless Pacemaker Synchronization Based on Conductive Intracardiac Communication" by A. Ryser et al. This work analyzed the bit error rate (BER) performance of a prototype dual-chamber leadless pacemaker both in simulation and in-vitro experiments on porcine hearts.

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Cryo-EM dataset of 80S ribosomes from yeast. This dataset has been described in Dashti et al. (2014, PNAS) "Trajectories of the ribosome as a Brownian nanomachine". In that study, a subset of the dataset was used to demonstrate the performance of a machine learning technique (now termed ManifoldEM) using manifold embedding to determine the energy landscape of a molecule. The dataset is re-analyzed in Seitz et al.

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