RESPECT, JING, AND PERSON

While respect for persons is fundamental to many moral and political theories, its nature and ground remain controversial. According to the standard model of respect, respect is primarily a response to certain inherent features of a person or an object. Importantly, it is in virtue of the value, status or authority of those features that respect is justified or owed. This model, however, faces many serious challenges. Drawing on the classical Confucian notion of jing (敬), I develop an alternative model of respect, which construes respect as an expression of agent’s sense of the self and its place in the world. The emphasis is thus on the agent’s own self-conception and the corresponding attitudes and dispositions, as opposed to on inherent features of the objects of respect. To further illustrate this distinction, I contrast the traditional Kantian conception of respect for persons with the Confucian, jing-based, conception along three dimensions: the normative ground, the content, and the distribution of respect. The Kantian respect certainly has its merits, and perhaps is indispensable in some contexts, but there is much more to respect than what Kantians can offer. The Confucian conception of respect, on the other hand, has important theoretical and practical values such that it is essential to an adequate understanding of the role of respect in our moral and political life

THE FIRST PERSON PERSPECTIVE: LANGUAGE, THOUGHT, AND ACTION

What it is to have a first person perspective? How do we come to understand our own perspective in the world? How do we take into account other people\u27s perspectives in our social and linguistic interactions? This dissertation is an exploration of these issues. But instead of approaching them in the abstract, it aims to shed light on these difficult questions through a series of case studies. First, I examine the role of the first person perspective in our agency, and explain the sense in which it is essential for action. Next, drawing on recent work in psychology, I propose an model of the development of temporal self-understanding in young children. Lastly, I develop a two-level pragmatic theory of epistemic modals

Fast B-spline Curve Fitting by L-BFGS

We propose a novel method for fitting planar B-spline curves to unorganized data points. In traditional methods, optimization of control points and foot points are performed in two very time-consuming steps in each iteration: 1) control points are updated by setting up and solving a linear system of equations; and 2) foot points are computed by projecting each data point onto a B-spline curve. Our method uses the L-BFGS optimization method to optimize control points and foot points simultaneously and therefore it does not need to perform either matrix computation or foot point projection in every iteration. As a result, our method is much faster than existing methods

Incremental Learning for Multi-organ Segmentation with Partially Labeled Datasets

There exists a large number of datasets for organ segmentation, which are partially annotated, and sequentially constructed. A typical dataset is constructed at a certain time by curating medical images and annotating the organs of interest. In other words, new datasets with annotations of new organ categories are built over time. To unleash the potential behind these partially labeled, sequentially-constructed datasets, we propose to learn a multi-organ segmentation model through incremental learning (IL). In each IL stage, we lose access to the previous annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs. We give the first attempt to conjecture that the different distribution is the key reason for 'catastrophic forgetting' that commonly exists in IL methods, and verify that IL has the natural adaptability to medical image scenarios. Extensive experiments on five open-sourced datasets are conducted to prove the effectiveness of our method and the conjecture mentioned above

LKCA: Large Kernel Convolutional Attention

We revisit the relationship between attention mechanisms and large kernel ConvNets in visual transformers and propose a new spatial attention named Large Kernel Convolutional Attention (LKCA). It simplifies the attention operation by replacing it with a single large kernel convolution. LKCA combines the advantages of convolutional neural networks and visual transformers, possessing a large receptive field, locality, and parameter sharing. We explained the superiority of LKCA from both convolution and attention perspectives, providing equivalent code implementations for each view. Experiments confirm that LKCA implemented from both the convolutional and attention perspectives exhibit equivalent performance. We extensively experimented with the LKCA variant of ViT in both classification and segmentation tasks. The experiments demonstrated that LKCA exhibits competitive performance in visual tasks. Our code will be made publicly available at https://github.com/CatworldLee/LKCA

Roles of microRNA on cancer cell metabolism

Advanced studies of microRNAs (miRNAs) have revealed their manifold biological functions, including control of cell proliferation, cell cycle and cell death. However, it seems that their roles as key regulators of metabolism have drawn more and more attention in the recent years. Cancer cells display increased metabolic autonomy in comparison to non-transformed cells, taking up nutrients and metabolizing them in pathways that support growth and proliferation. MiRNAs regulate cell metabolic processes through complicated mechanisms, including directly targeting key enzymes or transporters of metabolic processes and regulating transcription factors, oncogenes / tumor suppressors as well as multiple oncogenic signaling pathways. MiRNAs like miR-375, miR-143, miR-14 and miR-29b participate in controlling cancer cell metabolism by regulating the expression of genes whose protein products either directly regulate metabolic machinery or indirectly modulate the expression of metabolic enzymes, serving as master regulators, which will hopefully lead to a new therapeutic strategy for malignant cancer. This review focuses on miRNA regulations of cancer cell metabolism,including glucose uptake, glycolysis, tricarboxylic acid cycle and insulin production, lipid metabolism and amino acid biogenesis, as well as several oncogenic signaling pathways. Furthermore, the challenges of miRNA-based strategies for cancer diagnosis, prognosis and therapeutics have been discussed