Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs

As deep learning models nowadays are widely adopted by both cloud services and edge devices, reducing the latency of deep learning model inferences becomes crucial to provide efficient model serving. However, it is challenging to develop efficient tensor programs for deep learning operators due to the high complexity of modern accelerators and the rapidly growing number of operators. Deep learning compilers, such as Apache TVM, adopt declarative scheduling primitives to lower the bar of developing tensor programs. However, we show that this approach is insufficient to cover state-of-the-art tensor program optimizations. In this paper, we propose to embed the scheduling process into tensor programs and use dedicated mappings, called task mappings, to define the computation assignment and ordering. This new approach greatly enriches the expressible optimizations by allowing developers to manipulate tensor programs at a much finer granularity. We call the proposed method the task-mapping programming paradigm. In addition, we propose a new post-scheduling fusion optimization that allows developers to focus on scheduling every single operator and automates the fusion after scheduling. It greatly reduces the engineering efforts for operator fusion. Our proposed paradigm also constructs an efficient hardware-centric schedule space, which is agnostic to the program input size and greatly reduces the tuning time. With the proposed paradigm, we implement a deep learning compiler Hidet. Extensive experiments on modern convolution and transformer models show that Hidet outperforms state-of-the-art DNN inference framework, ONNX Runtime, and compiler, TVM equipped with scheduler AutoTVM and Ansor, by up to 1.48x (1.22x on average). It also reduces the tuning time by 20x and 11x compared with AutoTVM and Ansor, respectively. We open-sourced hidet at https://www.github.com/hidet-org/hidet.Comment: 15 pages, 22 figures, 1 tabl

Influences of mental accounting on consumption decisions: asymmetric effect of a scarcity mindset

A scarcity mindset is considered to impact consumer behaviors. Our research aimed to examine the moderating effect of the scarcity mindset on the relationship between mental accounting and hedonic (vs. utilitarian) consumption. We conducted an online experimental design (mental accounting: windfall gains vs. hard-earning gains; consumption: hedonic products vs. utilitarian products) and verified our hypotheses in two distinct samples: a student sample and an adult sample. Our results showed that consumers who received windfall gains tended to use it for hedonic consumption rather than utilitarian consumption. Intriguingly, such an effect was insignificant under a high level of a scarcity mindset but significant under a low level of the scarcity mindset. Moreover, consumers who received hard-earning gains tended to spend the money on utilitarian (vs. hedonic) consumption. However, we did not detect the impact of the scarcity mindset on such effects. Our research suggested an asymmetric effect of the scarcity mindset on hedonic (vs. utilitarian) consumption under two different mental accounts. It highlights the important role of the scarcity mindset in consumer behaviors, which leaves avenues for future research to understand marketing promotion strategies for distinct products

Automation-aided high-throughput technologies for synthetic biology

Synthetic biology is a research discipline which harnesses technological progress in de novo DNA synthesis as well as combining expertise of biological sciences and engineering research fields to facilitate construction of novel artificial biological systems. Since the past two decades, application of its methodologies has led to significant advances in metabolic engineering, providing alternative biochemical routes for the production of therapeutic products, cosmetics and biofuels. However, several challenges remain to be addressed to support development of synthetic biology applications, notably the demand for faster, cheaper and more reliable DNA manufacturing as well as efficient methods for genome-scale engineering of living organisms. This doctoral thesis proposes new interdisciplinary approaches to these problems, taking advantage of the latest laboratory automation technologies to improve efficiency of modern DNA assembly and genome editing methods. The first results chapter proposes application of a robotic platform for an acoustic liquid transfer for miniaturisation of DNA fabrication. This research, published in 2016, demonstrates the possibility to cost-efficiently assemble DNA in sub-microlitre assembly reactions. The second results chapter presents efforts to develop a method for genome-scale engineering of a model eukaryote, the budding yeast. This work capitalises on the recent progress in on-chip DNA synthesis and the next-generation sequencing (NGS) technology. Finally, the last results chapter demonstrates computational studies to predict and accelerate turnaround times of a commercial DNA supply chain using probabilistic simulations. The developed software is used to estimate sequence-specific DNA manufacturing turnaround times in order to help plan DNA manufacturing and guide decisions regarding further automation of different experimental procedures

Temporal transcriptome profiling of developing seeds reveals a concerted gene regulation in relation to oil accumulation in Pongamia (Millettia pinnata)

Background Pongamia (Millettia pinnata syn. Pongamia pinnata), an oilseed legume species, is emerging as potential feedstock for sustainable biodiesel production. Breeding Pongamia for favorable traits in commercial application will rely on a comprehensive understanding of molecular mechanism regulating oil accumulation during its seed development. To date, only limited genomic or transcript sequences are available for Pongamia, while a temporal transcriptome profiling of developing seeds is still lacking in this species. Results In this work, we conducted a time-series analysis of morphological and physiological characters, oil contents and compositions, as well as global gene expression profiles in developing Pongamia seeds. Firstly, three major developmental phases were characterized based on the combined evidences from embryonic shape, seed weight, seed moisture content, and seed color. Then, the gene expression levels at these three phases were quantified by RNA-Seq analyses with three biological replicates from each phase. Nearly 94% of unigenes were expressed at all three phases, whereas only less than 2% of unigenes were exclusively expressed at one of these phases. A total of 8881 differentially expressed genes (DEGs) were identified between phases. Furthermore, the qRT-PCR analyses for 10 DEGs involved in lipid metabolism demonstrated a good reliability of our RNA-Seq data in temporal gene expression profiling. We observed a dramatic increase in seed oil content from the embryogenesis phase to the early seed-filling phase, followed by a steady and moderate increase towards the maximum at the desiccation phase. We proposed that a highly active expression of most genes related to fatty acid (FA) and triacylglycerol (TAG) biosynthesis at the embryogenesis phase might trigger both the substantial oil accumulation and the membrane lipid synthesis for rapid cell proliferation at this phase, while a concerted reactivation of TAG synthesis-related genes at the desiccation phase might further promote storage lipid synthesis to achieve the maximum content of seed oils. Conclusions This study not only built a bridge between gene expression profiles and oil accumulation in developing seeds, but also laid a foundation for future attempts on genetic engineering of Pongamia varieties to acquire higher oil yield or improved oil properties for biofuel applications

PathAsst: Redefining Pathology through Generative Foundation AI Assistant for Pathology

As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, with significant applications in natural image interpretation. However, the field of pathology has largely remained untapped in this regard, despite the growing need for accurate, timely, and personalized diagnostics. To bridge the gap in pathology MLLMs, we present the PathAsst in this study, which is a generative foundation AI assistant to revolutionize diagnostic and predictive analytics in pathology. To develop PathAsst, we collect over 142K high-quality pathology image-text pairs from a variety of reliable sources, including PubMed, comprehensive pathology textbooks, reputable pathology websites, and private data annotated by pathologists. Leveraging the advanced capabilities of ChatGPT/GPT-4, we generate over 180K instruction-following samples. Furthermore, we devise additional instruction-following data, specifically tailored for the invocation of the pathology-specific models, allowing the PathAsst to effectively interact with these models based on the input image and user intent, consequently enhancing the model's diagnostic capabilities. Subsequently, our PathAsst is trained based on Vicuna-13B language model in coordination with the CLIP vision encoder. The results of PathAsst show the potential of harnessing the AI-powered generative foundation model to improve pathology diagnosis and treatment processes. We are committed to open-sourcing our meticulously curated dataset, as well as a comprehensive toolkit designed to aid researchers in the extensive collection and preprocessing of their own datasets. Resources can be obtained at https://github.com/superjamessyx/Generative-Foundation-AI-Assistant-for-Pathology.Comment: 13 pages, 5 figures, conferenc

Assessment of multi-source observation merged 1 km-grid precipitation product during the disastrous rainstorms in Guangdong

This paper aims to assess the latest 1 km-grid Analysis Real Time (ART_1 km) precipitation product developed by the National Meteorological Information Center of China Meteorological Administration (CMA), which can provide great support for disaster weather monitoring and warning, intelligent grid forecasting and weather services. Observed precipitation data from the independent stations (including non-uploaded regional meteorological stations and hydrometric stations) that were not integrated into the ART_1 km precipitation product as well as precipitation classification inspection are used to assess the quality of this product during twenty disastrous rainstorm cases from May to August during 2019-2022 in Guangdong. The results show that the ART_1 km precipitation product successfully reproduces the precipitation location, strength, and trends in these cases, with the best performance in the Pearl River Delta, the east of eastern Guangdong, and the north of northern Guangdong. The stronger the precipitation, the greater the correlation as well as the root mean square error (RMSE) and mean error (ME) between the ART_1 km precipitation and the observed precipitation. When the hourly precipitation is not classified, about 60% of these independent stations present a correlation efficient ≥ 0.8, more than 90% of the stations present an RMSE within the range of [1.0, 5.0) mm, and more than 60% of the stations present a ME within ±0.1 mm. When the hourly precipitation is < 5 mm, most of the stations have a correlation efficient < 0.5, an RMSE within the range of [1.0, 5.0) mm, and a ME within [0.0, 0.5] mm. When the hourly precipitation is ≥ 20 mm, 42%~56% of the stations have a correlation efficient ≥ 0.5, and most of the stations have an RMSE ≥ 10 mm and a ME < 0 mm, even when the hourly precipitation is ≥ 50 mm, most of the stations have a ME < -10 mm. Overall, ART_1 km precipitation is usually underestimated at the independent stations, and integrating observations from more sites into producing ART_1 km precipitation is helpful to improve the quality of the products

Progress in the study of aging marker criteria in human populations

The use of human aging markers, which are physiological, biochemical and molecular indicators of structural or functional degeneration associated with aging, is the fundamental basis of individualized aging assessments. Identifying methods for selecting markers has become a primary and vital aspect of aging research. However, there is no clear consensus or uniform principle on the criteria for screening aging markers. Therefore, we combine previous research from our center and summarize the criteria for screening aging markers in previous population studies, which are discussed in three aspects: functional perspective, operational implementation perspective and methodological perspective. Finally, an evaluation framework has been established, and the criteria are categorized into three levels based on their importance, which can help assess the extent to which a candidate biomarker may be feasible, valid, and useful for a specific use context