Promises, Promises

As host of the Olympic Games, China seeks to increase national economic and socialdevelopment and "display to the world a new image of China", and presents the Games as an opportunity to foster democracy, improve human rights and integrate China with the rest of the world. In its Olympic Action Plan promulgated in 2002, China outlined the phases of construction in the run up to the 2008 Games, and the standards to which it would hold itself in the governance and construction of venues, impact on Beijing's environment, increasing social and economic development and providing China's citizenry with greater access to information and technology.The goals and specific commitments that the government has adopted not only have implications for the smooth and successfuloperation of the Olympic Games, but also have the potential to impact on a number of China's international obligations, including its human rights obligations.Despite human rights-related commitments as diverse as transparency and accountability, access to information and freedom of the press, poverty alleviation, an improved standard of living for all people, and compensation for evictions and health issues, the record to date raises serious compliance issues

Cancer-Net PCa-Data: An Open-Source Benchmark Dataset for Prostate Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data

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

Probing the Superfluid to Mott Insulator Transition at the Single Atom Level

Quantum gases in optical lattices offer an opportunity to experimentally realize and explore condensed matter models in a clean, tunable system. We investigate the Bose-Hubbard model on a microscopic level using single atom-single lattice site imaging; our technique enables space- and time-resolved characterization of the number statistics across the superfluid-Mott insulator quantum phase transition. Site-resolved probing of fluctuations provides us with a sensitive local thermometer, allows us to identify microscopic heterostructures of low entropy Mott domains, and enables us to measure local quantum dynamics, revealing surprisingly fast transition timescales. Our results may serve as a benchmark for theoretical studies of quantum dynamics, and may guide the engineering of low entropy phases in a lattice

Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images

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 compute 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. 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

Personality as a Predictor of Student Success in Programming Principles

Large numbers of college students continue to fail to successfully complete programming principles courses. However, little research has addressed potential reasons for student failure. Many educators simply assume that high failure rates are acceptable – that computer programming is difficult and some students simply “don’t get it.” Some researchers (i.e., Bishop-Clark & Wheeler, 1994; Carland & Carland, 1990) have studied personality as a predictor of success in computer programming courses. However, with the exception of Woszczynski & Guthrie (2003), few studies have attempted to gather cognitive profiles (Krause, 2000) and match performance to profile type exhibited. Krause’s work shows that students with identified profiles can apply certain study skills to improve the probability of success in the classroom, and Woszczynski & Guthrie (2003) extended this research to the programming classroom, identifying underperforming cognitive profile groups. This study identified the primary cognitive profile of 236 students in a programming principles course at a southeastern university and matched profile to final average in programming principles I. Overall, intuitive thinkers (NT) tended to perform better in programming principles I than sensor feelers (SF). We found no other differences in performance between other paired profiles. We recommend a number of interventions to reach underperforming groups

Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted Imaging Data via Anatomic-Conditional Controlled Latent Diffusion

In Canada, prostate cancer is the most common form of cancer in men and accounted for 20% of new cancer cases for this demographic in 2022. Due to recent successes in leveraging machine learning for clinical decision support, there has been significant interest in the development of deep neural networks for prostate cancer diagnosis, prognosis, and treatment planning using diffusion weighted imaging (DWI) data. A major challenge hindering widespread adoption in clinical use is poor generalization of such networks due to scarcity of large-scale, diverse, balanced prostate imaging datasets for training such networks. In this study, we explore the efficacy of latent diffusion for generating realistic prostate DWI data through the introduction of an anatomic-conditional controlled latent diffusion strategy. To the best of the authors' knowledge, this is the first study to leverage conditioning for synthesis of prostate cancer imaging. Experimental results show that the proposed strategy, which we call Cancer-Net PCa-Gen, enhances synthesis of diverse prostate images through controllable tumour locations and better anatomical and textural fidelity. These crucial features make it well-suited for augmenting real patient data, enabling neural networks to be trained on a more diverse and comprehensive data distribution. The Cancer-Net PCa-Gen framework and sample images have been made publicly available at https://www.kaggle.com/datasets/deetsadi/cancer-net-pca-gen-dataset as a part of a global open-source initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer

NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches

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) on https://www.kaggle.com/nutritionverse/datasets as part of an open initiative to accelerate machine learning for dietary sensing