Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation

The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have difficulty in balancing the scalability and performance. In this paper, we propose a hard-aware instance adaptive self-training framework for UDA on the task of semantic segmentation. To effectively improve the quality and diversity of pseudo-labels, we develop a novel pseudo-label generation strategy with an instance adaptive selector. We further enrich the hard class pseudo-labels with inter-image information through a skillfully designed hard-aware pseudo-label augmentation. Besides, we propose the region-adaptive regularization to smooth the pseudo-label region and sharpen the non-pseudo-label region. For the non-pseudo-label region, consistency constraint is also constructed to introduce stronger supervision signals during model optimization. Our method is so concise and efficient that it is easy to be generalized to other UDA methods. Experiments on GTA5 to Cityscapes, SYNTHIA to Cityscapes, and Cityscapes to Oxford RobotCar demonstrate the superior performance of our approach compared with the state-of-the-art methods.Comment: arXiv admin note: text overlap with arXiv:2008.1219

DISPEL: Domain Generalization via Domain-Specific Liberating

Domain generalization aims to learn a generalization model that can perform well on unseen test domains by only training on limited source domains. However, existing domain generalization approaches often bring in prediction-irrelevant noise or require the collection of domain labels. To address these challenges, we consider the domain generalization problem from a different perspective by categorizing underlying feature groups into domain-shared and domain-specific features. Nevertheless, the domain-specific features are difficult to be identified and distinguished from the input data. In this work, we propose DomaIn-SPEcific Liberating (DISPEL), a post-processing fine-grained masking approach that can filter out undefined and indistinguishable domain-specific features in the embedding space. Specifically, DISPEL utilizes a mask generator that produces a unique mask for each input data to filter domain-specific features. The DISPEL framework is highly flexible to be applied to any fine-tuned models. We derive a generalization error bound to guarantee the generalization performance by optimizing a designed objective loss. The experimental results on five benchmarks demonstrate DISPEL outperforms existing methods and can further generalize various algorithms

Towards Assumption-free Bias Mitigation

Despite the impressive prediction ability, machine learning models show discrimination towards certain demographics and suffer from unfair prediction behaviors. To alleviate the discrimination, extensive studies focus on eliminating the unequal distribution of sensitive attributes via multiple approaches. However, due to privacy concerns, sensitive attributes are often either unavailable or missing in real-world scenarios. Therefore, several existing works alleviate the bias without sensitive attributes. Those studies face challenges, either in inaccurate predictions of sensitive attributes or the need to mitigate unequal distribution of manually defined non-sensitive attributes related to bias. The latter requires strong assumptions about the correlation between sensitive and non-sensitive attributes. As data distribution and task goals vary, the strong assumption on non-sensitive attributes may not be valid and require domain expertise. In this work, we propose an assumption-free framework to detect the related attributes automatically by modeling feature interaction for bias mitigation. The proposed framework aims to mitigate the unfair impact of identified biased feature interactions. Experimental results on four real-world datasets demonstrate that our proposed framework can significantly alleviate unfair prediction behaviors by considering biased feature interactions

Engulfing cells promote neuronal regeneration and remove neuronal debris through distinct biochemical functions of CED-1

Two important biological events happen coincidently soon after nerve injury in the peripheral nervous system in C. elegans: removal of axon debris and initiation of axon regeneration. But, it is not known how these two events are co-regulated. Mutants of ced-1, a homolog of Draper and MEGF10, display defects in both events. One model is that those events could be related. But our data suggest that they are actually separable. CED-1 functions in the muscle-type engulfing cells in both events and is enriched in muscle protrusions in close contact with axon debris and regenerating axons. Its two functions occur through distinct biochemical mechanisms; extracellular domain-mediated adhesion for regeneration and extracellular domain binding-induced intracellular domain signaling for debris removal. These studies identify CED-1 in engulfing cells as a receptor in debris removal but as an adhesion molecule in neuronal regeneration, and have important implications for understanding neural circuit repair after injury

Chinese Medicinal Herbs for Childhood Pneumonia: A Systematic Review of Effectiveness and Safety

Objective. To assess the efficacy and safety of Chinese medicinal herbs for Childhood Pneumonia. Methods. We included randomized controlled trials (RCTs). The searched electronic databases included PubMed, the Cochrane Central Register of Controlled Trials, EMBASE, CBM, CNKI, and VIP. All studies included were assessed for quality and risk bias. Review Manager 5.1.6 software was used for data analyses, and the GRADEprofiler software was applied to classify the systematic review results. Results. Fourteen studies were identified (n=1.824). Chinese herbs may increase total effective rate (risk ratio (RR) 1.18; 95% confidence interval (CI), 1.11–1.26) and improve cough (total mean difference (MD), −2.18; 95% CI, (−2.66)–(−1.71)), fever (total MD, −1.85; 95% CI, (−2.29)–(−1.40)), rales (total MD, −1.53; 95% CI, (−1.84)–(−1.23)), and chest films (total MD, −3.10; 95% CI, (−4.11)–(−2.08)) in Childhood Pneumonia. Chinese herbs may shorten the length of hospital stay (total MD, −3.00; 95% CI, (−3.52)–(−2.48)), but no significant difference for adverse effects (RR, 0.39; 95% CI, 0.09–1.72) was identified. Conclusion. Chinese herbs may increase total effective rate and improve symptoms and signs. However, large, properly randomized, placebo-controlled, double-blind studies are required

Mutual-anonymity and Authentication Key Agreement Protocol

Abstract: According to the characteristics of trusted computation, we proposed an efficient pseudonym ring signature-based authentication and key agreement protocol with mutual anonymity. The use of ring signature can hide the identity information of communicating parties and effectively prevent the leakage of private information. Finally we derive a shared session key between them for their future secure communication especially in the trusted computation environment. Our protocol reaches the level of universally composable security and is more efficient

System-Level Biochip for Impedance Sensing and Programmable Manipulation of Bladder Cancer Cells

This paper develops a dielectrophoretic (DEP) chip with multi-layer electrodes and a micro-cavity array for programmable manipulations of cells and impedance measurement. The DEP chip consists of an ITO top electrode, flow chamber, middle electrode on an SU-8 surface, micro-cavity arrays of SU-8 and distributed electrodes at the bottom of the micro-cavity. Impedance sensing of single cells could be performed as follows: firstly, cells were trapped in a micro-cavity array by negative DEP force provided by top and middle electrodes; then, the impedance measurement for discrimination of different stage of bladder cancer cells was accomplished by the middle and bottom electrodes. After impedance sensing, the individual releasing of trapped cells was achieved by negative DEP force using the top and bottom electrodes in order to collect the identified cells once more. Both cell manipulations and impedance measurement had been integrated within a system controlled by a PC-based LabVIEW program. In the experiments, two different stages of bladder cancer cell lines (grade III: T24 and grade II: TSGH8301) were utilized for the demonstration of programmable manipulation and impedance sensing; as the results show, the lower-grade bladder cancer cells (TSGH8301) possess higher impedance than the higher-grade ones (T24). In general, the multi-step manipulations of cells can be easily programmed by controlling the electrical signal in our design, which provides an excellent platform technology for lab-on-a-chip (LOC) or a micro-total-analysis-system (Micro TAS)