Classical cryptographic schemes in use today are based on the difficulty of certain number theoretic problems. Security is guaranteed by the fact that the computational work required to break the core mechanisms of these schemes on a conventional computer is infeasible; however, the difficulty of these problems would not withstand the computational power of a large-scale quantum computer. To this end, the post-quantum cryptography (PQC) standardization process initiated by the National Institute of Standards and Technology (NIST) is well underway. In addition to the evaluation criteria provided by NIST, the energy consumption of these candidate algorithms is also an important criterion to consider due to the use of battery-operated devices, high-performance computing environments where energy costs are critical, as well as in the interest of green computing. In this paper, the energy consumption of PQC candidates is evaluated on an Intel Core i7-6700 CPU using PAPI, the Performance API. The energy measurements are categorized based on their proposed security level and cryptographic functionality. The results are then further subdivided based on the underlying mechanism used in order to identify the most energy-efficient schemes. Lastly, IgProf is used to identify the most energy-consuming subroutines within a select number of submissions to highlight potential areas for optimization.

Accurate dietary intake estimation is critical for informing policies and programs to support healthy eating, as malnutrition has been directly linked to decreased quality of life. However self-reporting methods such as food diaries suffer from substantial bias. Other conventional dietary assessment techniques and emerging alternative approaches such as mobile applications incur high time costs and may necessitate trained personnel. Recent work has focused on using computer vision and machine learning to automatically estimate dietary intake from food images, but the lack of comprehensive datasets with diverse viewpoints, modalities and food annotations hinders the accuracy and realism of such methods. To address this limitation, we introduce NutritionVerse-Synth, the first large-scale dataset of 84,984 photorealistic synthetic 2D food images with associated dietary information and multimodal annotations (including depth images, instance masks, and semantic masks). Additionally, we collect a real image dataset, NutritionVerse-Real, containing 889 images of 251 dishes to evaluate realism. Leveraging these novel datasets, we develop and benchmark NutritionVerse, an empirical study of various dietary intake estimation approaches, including indirect segmentation-based and direct prediction networks. We further fine-tune models pretrained on synthetic data with real images to provide insights into the fusion of synthetic and real data. Finally, we release both datasets (NutritionVerse-Synth, NutritionVerse-Real) and the collection of models (NutritionVerse) on https://bit.ly/genai4good as part of an open initiative to accelerate machine learning for dietary sensing.

Skin cancer is the most common type of cancer in the United States and is estimated to affect one in five Americans. Recent advances have demonstrated strong performance on skin cancer detection, as exemplified by state of the art performance in the SIIM-ISIC Melanoma Classification Challenge; however, these solutions leverage ensembles of complex deep neural architectures requiring immense storage and computation costs, and therefore may not be tractable. A recent movement for TinyML applications is integrating Double-Condensing Attention Condensers (DC-AC) into a self-attention neural network backbone architecture to allow for faster and more efficient computation. This paper explores leveraging an efficient self-attention structure to detect skin cancer in skin lesion images and introduces a deep neural network design with DC-AC customized for skin cancer detection from skin lesion images. We demonstrate that our approach with only 1.6 million parameters and 0.32 GFLOPs achieves better performance compared to traditional architecture designs as it obtains an area under the ROC curve of 0.90 on the public ISIC 2020 test set and 0.89 on the private ISIC test set, over 0.13 above the best Cancer-Net SCa network architecture design. The final model is publicly available as a part of a global open-source initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer. Future work of this research includes iterating on the design of the selected network architecture and refining the approach to generalize to other forms of cancer.

The recent introduction of synthetic correlated diffusion (CDI$^s$) imaging has demonstrated significant potential in the realm of clinical decision support for prostate cancer (PCa). CDI$^s$ is a new form of magnetic resonance imaging (MRI) designed to characterize tissue characteristics through the joint correlation of diffusion signal attenuation across different Brownian motion sensitivities. Despite the performance improvement, the CDI$^s$ data for PCa has not been previously made publicly available. In our commitment to advance research efforts for PCa, we introduce Cancer-Net PCa-Data, an open-source benchmark dataset of volumetric CDI$^s$ imaging data of PCa patients. Cancer-Net PCa-Data consists of CDI$^s$ volumetric images from a patient cohort of 200 patient cases, along with full annotations (gland masks, tumor masks, and PCa diagnosis for each tumor). We also analyze the demographic and label region diversity of Cancer-Net PCa-Data for potential biases. Cancer-Net PCa-Data is the first-ever public dataset of CDI$^s$ imaging data for PCa, and is a part of the global open-source initiative dedicated to advancement in machine learning and imaging research to aid clinicians in the global fight against cancer.

Elderly populations often face significant challenges when it comes to dietary intake tracking, often exacerbated by health complications. Unfortunately, conventional diet assessment techniques such as food frequency questionnaires, food diaries, and 24 h recall are subject to substantial bias. Recent advancements in machine learning and computer vision show promise of automated nutrition tracking methods of food, but require a large, high-quality dataset in order to accurately identify the nutrients from the food on the plate. However, manual creation of large-scale datasets with such diversity is time-consuming and hard to scale. On the other hand, synthesized 3D food models enable view augmentation to generate countless photorealistic 2D renderings from any viewpoint, reducing imbalance across camera angles. In this paper, we present a process to collect a large image dataset of food scenes that span diverse viewpoints and highlight its usage in dietary intake estimation. We first collect quality 3D objects of food items (NV-3D) that are used to generate photorealistic synthetic 2D food images (NV-Synth) and then manually collect a validation 2D food image dataset (NV-Real). We benchmark various intake estimation approaches on these datasets and present NutritionVerse3D2D, a collection of datasets that contain 3D objects and 2D images, along with models that estimate intake from the 2D food images. We release all the datasets along with the developed models to accelerate machine learning research on dietary sensing.