Network-Aware Asynchronous Distributed ADMM Algorithm for Peer-to-Peer Energy Trading

The increasing uptake of distributed energy resources (DERs) in smart home prosumers calls for distributed energy management strategies, and the advances in information and communications technology enable peer-to-peer (P2P) energy trading and transactive energy management. Many works attempted to solve the transactive energy management problem using distributed optimization to preserve the privacy of DERs' operations. But such distributed optimization requires information exchange among prosumers, often via synchronous communications, which can be unrealistic in practice. This paper addresses a transactive energy trading problem for multiple smart home prosumers with rooftop solar, battery storage, and controllable load, such as heating, ventilation, and air-conditioning (HVAC) units, considering practical communication conditions. We formulate a network-aware energy trading optimization problem, in which a local network operator manages the network constraints supporting bidirectional energy flows. We develop an asynchronous distributed alternating direction method of multipliers (ADMM) algorithm to solve the problem under asynchronous communications, allowing communication delay and indicating a higher potential for real-world applications. We validate our design by simulations using real-world data. The results demonstrate the convergence of our developed asynchronous distributed ADMM algorithm and show that energy trading reduces the energy cost for smart home prosumers

Color-Kinematics Duality with Minimal Deformation: Two-Loop Four-Gluon Amplitudes in Pure Yang-Mills Revisited

The conjectured duality between color and kinematics has significantly advanced our understanding of both gauge and gravitational theories. However, constructing numerators that manifest the color-kinematics (CK) duality, even for the two-loop four-gluon amplitude in pure Yang-Mills, has been challenging. In this paper, we revisit this amplitude and show that the difficulty of applying CK duality can be overcome by introducing a simple deformation. Our approach distinguishes itself from previous studies by maximizing the use of off-shell CK duality while maintaining a compact ansatz. In particular, the deformation we introduce satisfies a subset of off-shell dual Jacobi relations. The resulting numerators are presented in $d$-dimensionally Lorentz invariant local form and are applicable to all helicities of external gluons. The solution we provide can be directly employed to construct the corresponding gravitational amplitude through double copy. Our findings suggest a novel and efficient strategy for constructing high-loop gauge and gravitational amplitudes using CK duality.Comment: 24 pages, 11 figure

Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.Comment: 30 pages, 19 figures, submitted to CBM journa

Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation

This study aims to develop and evaluate an innovative simulation algorithm for generating thick-slice CT images that closely resemble actual images in the AAPM-Mayo's 2016 Low Dose CT Grand Challenge dataset. The proposed method was evaluated using Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE) metrics, with the hypothesis that our simulation would produce images more congruent with their real counterparts. Our proposed method demonstrated substantial enhancements in terms of both PSNR and RMSE over other simulation methods. The highest PSNR values were obtained with the proposed method, yielding 49.7369 $\pm$ 2.5223 and 48.5801 $\pm$ 7.3271 for D45 and B30 reconstruction kernels, respectively. The proposed method also registered the lowest RMSE with values of 0.0068 $\pm$ 0.0020 and 0.0108 $\pm$ 0.0099 for D45 and B30, respectively, indicating a distribution more closely aligned with the authentic thick-slice image. Further validation of the proposed simulation algorithm was conducted using the TCIA LDCT-and-Projection-data dataset. The generated images were then leveraged to train four distinct super-resolution (SR) models, which were subsequently evaluated using the real thick-slice images from the 2016 Low Dose CT Grand Challenge dataset. When trained with data produced by our novel algorithm, all four SR models exhibited enhanced performance.Comment: 11 pages, 4 figure

Part-time Power Measurements: nvidia-smi's Lack of Attention

The GPU has emerged as the go-to accelerator for high throughput and parallel workloads, spanning scientific simulations to AI, thanks to its performance and power efficiency. Given that 6 out of the top 10 fastest supercomputers in the world use NVIDIA GPUs and many AI companies each employ 10,000's of NVIDIA GPUs, an accurate understanding of GPU power consumption is essential for making progress to further improve its efficiency. Despite the limited documentation and the lack of understanding of its mechanisms, NVIDIA GPUs' built-in power sensor, providing easily accessible power readings via the nvidia-smi interface, is widely used in energy efficient computing research on GPUs. Our study seeks to elucidate the internal mechanisms of the power readings provided by nvidia-smi and assess the accuracy of the power and energy consumption data. We have developed a suite of micro-benchmarks to profile the behaviour of nvidia-smi power readings and have evaluated them on over 70 different GPUs from all architectural generations since power measurement was first introduced in the 'Fermi' generation. We have identified several unforeseen problems in terms of power/energy measurement using nvidia-smi, for example on the A100 and H100 GPUs only 25% of the runtime is sampled for power consumption, during the other 75% of the time, the GPU can be using drastically different power and nvidia-smi and results presented by it are unaware of this. This along with other findings can lead to a drastic under/overestimation of energy consumed, especially when considering data centres housing tens of thousands of GPUs. We proposed several good practices that help to mitigate these problems. By comparing our results to those measured from an external power-meter, we have reduced the error in the energy measurement by an average of 35% and in some cases by as much as 65% in the test cases we present