Identifiability of Label Noise Transition Matrix

The noise transition matrix plays a central role in the problem of learning with noisy labels. Among many other reasons, a large number of existing solutions rely on access to it. Identifying and estimating the transition matrix without ground truth labels is a critical and challenging task. When label noise transition depends on each instance, the problem of identifying the instance-dependent noise transition matrix becomes substantially more challenging. Despite recent works proposing solutions for learning from instance-dependent noisy labels, the field lacks a unified understanding of when such a problem remains identifiable. The goal of this paper is to characterize the identifiability of the label noise transition matrix. Building on Kruskal's identifiability results, we are able to show the necessity of multiple noisy labels in identifying the noise transition matrix for the generic case at the instance level. We further instantiate the results to explain the successes of the state-of-the-art solutions and how additional assumptions alleviated the requirement of multiple noisy labels. Our result also reveals that disentangled features are helpful in the above identification task and we provide empirical evidence.Comment: Preprint. Under review. For questions please contact [email protected]

Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

Conventional change detection approaches are mainly based on per-pixel processing, which ignore the sub-pixel spectral variation resulted from spectral mixture. Especially for medium-resolution remote sensing images used in urban land-cover change monitoring, land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution. Thus, traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably, degrading the overall accuracy of change detection. In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level, a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion. Nonlinear spectral mixture model is selected for spectral unmixing, and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple compositi..

Exploring Evaluation Factors and Framework for the Object of Automated Trading System

Automated trading system (ATS) is a computer program that combines different trading rules to find optimal trading opportunities. The objects of ATS, which are financial assets, need evaluation because that is of great significance for stakeholders and market orders. From the perspectives of dealers, agents, external environment, and objects themselves, this study explored factors in evaluating and choosing the object of ATS. Based on design science research (DSR), we presented a preliminary evaluation framework and conducted semi-structured interviews with twelve trading participants engaged in different occupations. By analyzing the data collected, we validated eight factors from literatures and found four new factors and fifty-four sub-factors. Additionally, this paper developed a relationship model of factors. The results could be used in future work to explore and validate more evaluation factors by using data mining

An enzyme-responsive conjugate improves the delivery of a PI3K inhibitor to prostate cancer

AbstractAn enzyme-responsive peptide drug conjugate was developed for TGX-D1, a promising PI3K inhibitor for prostate cancer therapy. LNCaP-specific KYL peptide was used as the targeting ligand and the prostate specific antigen (PSA) cleavable peptide (SSKYQSL) was used as the enzyme-responsive linker. SSKYQSL is cleaved by recombinant human PSA at 10–250 μg/mL. By contrast, the linker is stable in the serum of prostate cancer patients with high PSA levels (>500 ng/mL), indicating that this linker can survive the systemic circulation in prostate cancer patients but be cleaved in the tumor microenvironment. Cellular uptake of the peptide drug conjugate in prostate cancer cells is improved by about nine times. Biodistribution study reveals significant tumor accumulation of the peptide drug conjugate in nude mice bearing C4–2 tumor xenografts. Meanwhile, distribution of the conjugate in other major tissues is the same as the parent drug, indicating a high specificity of the conjugate to prostate cancers in vivo