A Counterexample for the Principal Eigenvalue of An Elliptic Operator with Large Advection

There are numerous studies focusing on the convergence of the principal eigenvalue $\lambda(s)$ as $s\to+\infty$ corresponding to the elliptic eigenvalue problem \begin{align*} -\Delta\varphi(x)-2s\mathbf{v}\cdot\nabla\varphi(x)+c(x)\varphi(x)=\lambda(s)\varphi(x),\quad x\in \Omega, \end{align*} where $\Omega$ is a bounded domain and the advection term $\mathbf{v}$ under some certain restrictions. In this paper, we construct an infinitely oscillating gradient advection term $\mathbf{v}=\nabla m(x)\in C^1(\Omega)$ such that the principal eigenvalue $\lambda(s)$ does not converge as $s\to+\infty$. As far as we know, this is the first result that guarantee the non-convergence of the principal eigenvalue

Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction

Survival outcome assessment is challenging and inherently associated with multiple clinical factors (e.g., imaging and genomics biomarkers) in cancer. Enabling multimodal analytics promises to reveal novel predictive patterns of patient outcomes. In this study, we propose a multimodal transformer (PathOmics) integrating pathology and genomics insights into colon-related cancer survival prediction. We emphasize the unsupervised pretraining to capture the intrinsic interaction between tissue microenvironments in gigapixel whole slide images (WSIs) and a wide range of genomics data (e.g., mRNA-sequence, copy number variant, and methylation). After the multimodal knowledge aggregation in pretraining, our task-specific model finetuning could expand the scope of data utility applicable to both multi- and single-modal data (e.g., image- or genomics-only). We evaluate our approach on both TCGA colon and rectum cancer cohorts, showing that the proposed approach is competitive and outperforms state-of-the-art studies. Finally, our approach is desirable to utilize the limited number of finetuned samples towards data-efficient analytics for survival outcome prediction. The code is available at https://github.com/Cassie07/PathOmics.Comment: Accepted to MICCAI2023 (Top14%

Deep Imaging of the HCG 95 Field.I.Ultra-diffuse Galaxies

We present a detection of 89 candidates of ultra-diffuse galaxies (UDGs) in a 4.9 degree$^2$ field centered on the Hickson Compact Group 95 (HCG 95) using deep $g$- and $r$-band images taken with the Chinese Near Object Survey Telescope. This field contains one rich galaxy cluster (Abell 2588 at $z$=0.199) and two poor clusters (Pegasus I at $z$=0.013 and Pegasus II at $z$=0.040). The 89 candidates are likely associated with the two poor clusters, giving about 50 $-$ 60 true UDGs with a half-light radius $r_{\rm e} > 1.5$ kpc and a central surface brightness $\mu(g,0) > 24.0$ mag arcsec$^{-2}$. Deep $z$'-band images are available for 84 of the 89 galaxies from the Dark Energy Camera Legacy Survey (DECaLS), confirming that these galaxies have an extremely low central surface brightness. Moreover, our UDG candidates are spread over a wide range in $g-r$ color, and $\sim$26% are as blue as normal star-forming galaxies, which is suggestive of young UDGs that are still in formation. Interestingly, we find that one UDG linked with HCG 95 is a gas-rich galaxy with H I mass $1.1 \times 10^{9} M_{\odot}$ detected by the Very Large Array, and has a stellar mass of $M_\star \sim 1.8 \times 10^{8}$ $M_{\odot}$. This indicates that UDGs at least partially overlap with the population of nearly dark galaxies found in deep H I surveys. Our results show that the high abundance of blue UDGs in the HCG 95 field is favored by the environment of poor galaxy clusters residing in H I-rich large-scale structures.Comment: Published in Ap

Data-Centric Foundation Models in Computational Healthcare: A Survey

The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. The interactive nature of these models, guided by pre-training data and human instructions, has ignited a data-centric AI paradigm that emphasizes better data characterization, quality, and scale. In healthcare AI, obtaining and processing high-quality clinical data records has been a longstanding challenge, ranging from data quantity, annotation, patient privacy, and ethics. In this survey, we investigate a wide range of data-centric approaches in the FM era (from model pre-training to inference) towards improving the healthcare workflow. We discuss key perspectives in AI security, assessment, and alignment with human values. Finally, we offer a promising outlook of FM-based analytics to enhance the performance of patient outcome and clinical workflow in the evolving landscape of healthcare and medicine. We provide an up-to-date list of healthcare-related foundation models and datasets at https://github.com/Yunkun-Zhang/Data-Centric-FM-Healthcare

Livelihood resilience and livelihood construction path of China's rural reservoir resettled households in the energy transition

The construction of reservoirs has led to difficult livelihood transitions of resettled households after relocation and resettlement, resulting in a series of socioeconomic problems. How to scientifically integrate regional resource advantages and improve the livelihood resilience of resettled households has become an important problem to be solved to prevent and resolve social equity and justice risks and sustainable livelihood development. Taking Xiangjiaba Hydropower Station (Yunnan Reservoir area) as an example, the evaluation index system was constructed from the three dimensions of buffer capacity, self-organization capacity and learning capacity, the gray correlation degree method was used to evaluate index system, and the clustering method was selected to screen the indices that have a great impact on livelihood resilience. On this basis, the comprehensive index method was used to calculate the livelihood resilience, and the contribution degree model was used to identify the main contribution factors of livelihood resilience and to explore the path of livelihood construction to improve the livelihood resilience of resettled households. The results showed that (1) the level of livelihood resilience of resettled households was not high as a whole, in which the self-organization capacity was the strongest, the buffer capacity was the second strongest, and the learning capacity was the weakest; (2) there were differences in the livelihood resilience of resettled households who choose different livelihood modes. Among them, buffer capacity, learning capacity and livelihood resilience were characterized by wage operation type > part-time balanced type > agricultural operation type > subsidy dependent type, while self-organization capacity was characterized by agricultural operation type > part-time balanced type > wage operation type > subsidy dependent type. (3) The main contribution factors of the livelihood resilience of resettled households who choose different livelihood modes are similar. The number of laborers, participation in social organizations, skills training opportunities and 11 other main indices promote the formation and development of livelihood resilience from different dimensions. (4) The livelihood of resettled households should follow the construction path of “identifying transition capacity, clarifying resource advantages, strengthening livelihood support, and promoting livelihood reorganization” to promote the comprehensive improvement of livelihood resilience, ultimately achieving sustainable livelihood and high-level welfare. The livelihood resilience of resettled households is a continuous and dynamic development process, therefore, it is necessary to further improve the dynamic monitoring and evolution process of livelihood resilience, incorporate the impact of diverse data. This is also an important breakthrough in the study of livelihood resilience

OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models

The emergence of large language models (LLMs) has revolutionized natural language processing tasks. However, existing instruction-tuning datasets suffer from occupational bias: the majority of data relates to only a few occupations, which hampers the instruction-tuned LLMs to generate helpful responses to professional queries from practitioners in specific fields. To mitigate this issue and promote occupation-inclusive LLMs, we create an instruction-tuning dataset named \emph{OccuQuest}, which contains 110,000+ prompt-completion pairs and 30,000+ dialogues covering over 1,000 occupations in 26 occupational categories. We systematically request ChatGPT, organizing queries hierarchically based on Occupation, Responsibility, Topic, and Question, to ensure a comprehensive coverage of occupational specialty inquiries. By comparing with three commonly used datasets (Dolly, ShareGPT, and WizardLM), we observe that OccuQuest exhibits a more balanced distribution across occupations. Furthermore, we assemble three test sets for comprehensive evaluation, an occu-test set covering 25 occupational categories, an estate set focusing on real estate, and an occu-quora set containing real-world questions from Quora. We then fine-tune LLaMA on OccuQuest to obtain OccuLLaMA, which significantly outperforms state-of-the-art LLaMA variants (Vicuna, Tulu, and WizardLM) on professional questions in GPT-4 and human evaluations. Notably, on the occu-quora set, OccuLLaMA reaches a high win rate of 86.4\% against WizardLM