87 research outputs found
Content Individuation and Evolutionary Content Emergence
This short paper addresses two connected issues which were brought to some focused light by Searle’s comments on my contributed article to the anthology Searle’s philosophy and Chinese Philosophy: Constructive Engagement. The first issue concerns the claim that animals cannot have observer-independent intentional content of the same type as that of human beings. The second is my denial that mental content can be merely caused in specific brain states, given its holistic and normative character. I defend my position on the second issue by distinguishing content individuation from content realization while I elaborate my relatively more sophisticated argument for the first claim by clarifying two related senses or levels of ‘content’ and ‘self’, respectively associated with certain quasi-rational capacities from a third-person perspective and the subjective holistic consciousness from a first-person perspective with the explicit social-discursive dimension. Searle’s Connection Principle is briefly drawn on in this context, with an eye to showing its potential significance when it is extended into the evolutionary settings. In short, it is the full-blown rationality of human holistic discursive practice that ultimately grounds the content talk, which then becomes meaningfully ascribable to certain natural forms of animal existence
Smooth quasi-developable surfaces bounded by smooth curves
Computing a quasi-developable strip surface bounded by design curves finds
wide industrial applications. Existing methods compute discrete surfaces
composed of developable lines connecting sampling points on input curves which
are not adequate for generating smooth quasi-developable surfaces. We propose
the first method which is capable of exploring the full solution space of
continuous input curves to compute a smooth quasi-developable ruled surface
with as large developability as possible. The resulting surface is exactly
bounded by the input smooth curves and is guaranteed to have no
self-intersections. The main contribution is a variational approach to compute
a continuous mapping of parameters of input curves by minimizing a function
evaluating surface developability. Moreover, we also present an algorithm to
represent a resulting surface as a B-spline surface when input curves are
B-spline curves.Comment: 18 page
Memes, mind, and normativity
Prominent memeticists like Daniel Dennett and Susan Blackmore have made claims far more radical than those included in Dawkins’ original proposal, which provoked increasingly heated debates and arguments over the theoretical significance as well as limits or flaws of the entire memetic enterprise. In this paper, I examine closely some of the critical points taken by Kate Distin in her penetrating engagement with those radical claims, which include such ideas as the thought that we are meme machines as much as gene machines, the thesis that there is no conscious self inside those machines, and the claim that a complex interplay of replicators and environment is all there is to life (Blackmore 1999: 241). It is hoped that a viable thesis concerning a deep-seated normativity emerges from my discussion
EMS: 3D Eyebrow Modeling from Single-view Images
Eyebrows play a critical role in facial expression and appearance. Although
the 3D digitization of faces is well explored, less attention has been drawn to
3D eyebrow modeling. In this work, we propose EMS, the first learning-based
framework for single-view 3D eyebrow reconstruction. Following the methods of
scalp hair reconstruction, we also represent the eyebrow as a set of fiber
curves and convert the reconstruction to fibers growing problem. Three modules
are then carefully designed: RootFinder firstly localizes the fiber root
positions which indicates where to grow; OriPredictor predicts an orientation
field in the 3D space to guide the growing of fibers; FiberEnder is designed to
determine when to stop the growth of each fiber. Our OriPredictor is directly
borrowing the method used in hair reconstruction. Considering the differences
between hair and eyebrows, both RootFinder and FiberEnder are newly proposed.
Specifically, to cope with the challenge that the root location is severely
occluded, we formulate root localization as a density map estimation task.
Given the predicted density map, a density-based clustering method is further
used for finding the roots. For each fiber, the growth starts from the root
point and moves step by step until the ending, where each step is defined as an
oriented line with a constant length according to the predicted orientation
field. To determine when to end, a pixel-aligned RNN architecture is designed
to form a binary classifier, which outputs stop or not for each growing step.
To support the training of all proposed networks, we build the first 3D
synthetic eyebrow dataset that contains 400 high-quality eyebrow models
manually created by artists. Extensive experiments have demonstrated the
effectiveness of the proposed EMS pipeline on a variety of different eyebrow
styles and lengths, ranging from short and sparse to long bushy eyebrows.Comment: To appear in SIGGRAPH Asia 2023 (Journal Track). 19 pages, 19
figures, 6 table
HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling
In this work, we tackle the challenging problem of learning-based single-view
3D hair modeling. Due to the great difficulty of collecting paired real image
and 3D hair data, using synthetic data to provide prior knowledge for real
domain becomes a leading solution. This unfortunately introduces the challenge
of domain gap. Due to the inherent difficulty of realistic hair rendering,
existing methods typically use orientation maps instead of hair images as input
to bridge the gap. We firmly think an intermediate representation is essential,
but we argue that orientation map using the dominant filtering-based methods is
sensitive to uncertain noise and far from a competent representation. Thus, we
first raise this issue up and propose a novel intermediate representation,
termed as HairStep, which consists of a strand map and a depth map. It is found
that HairStep not only provides sufficient information for accurate 3D hair
modeling, but also is feasible to be inferred from real images. Specifically,
we collect a dataset of 1,250 portrait images with two types of annotations. A
learning framework is further designed to transfer real images to the strand
map and depth map. It is noted that, an extra bonus of our new dataset is the
first quantitative metric for 3D hair modeling. Our experiments show that
HairStep narrows the domain gap between synthetic and real and achieves
state-of-the-art performance on single-view 3D hair reconstruction.Comment: CVPR 2023 Highlight, project page:
https://paulyzheng.github.io/research/hairstep
Ferroptosis-related lncRNA signature predicts prognosis and immunotherapy efficacy in cutaneous melanoma
PurposeFerroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of this study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM.MethodsFerroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. The lncRNA signature was evaluated using the areas under the receiver operating characteristic curves (AUCs) and Kaplan-Meier analyses in the training, testing, and entire cohorts. Multivariate Cox regression analyses including the lncRNA signature and common clinicopathological characteristics were performed to identify independent predictors of overall survival (OS). A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed.ResultsWe identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups.ConclusionOur novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM
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