Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving

Deep neural networks (DNNs) have proven their capabilities in the past years and play a significant role in environment perception for the challenging application of automated driving. They are employed for tasks such as detection, semantic segmentation, and sensor fusion. Despite tremendous research efforts, several issues still need to be addressed that limit the applicability of DNNs in automated driving. The bad generalization of DNNs to unseen domains is a major problem on the way to a safe, large-scale application, because manual annotation of new domains is costly, particularly for semantic segmentation. For this reason, methods are required to adapt DNNs to new domains without labeling effort. This task is termed unsupervised domain adaptation (UDA). While several different domain shifts challenge DNNs, the shift between synthetic and real data is of particular importance for automated driving, as it allows the use of simulation environments for DNN training. We present an overview of the current state of the art in this research field. We categorize and explain the different approaches for UDA. The number of considered publications is larger than any other survey on this topic. We also go far beyond the description of the UDA state-of-the-art, as we present a quantitative comparison of approaches and point out the latest trends in this field. We conduct a critical analysis of the state-of-the-art and highlight promising future research directions. With this survey, we aim to facilitate UDA research further and encourage scientists to exploit novel research directions

Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving

Deep neural networks (DNNs) have proven their capabilities in the past years and play a significant role in environment perception for the challenging application of automated driving. They are employed for tasks such as detection, semantic segmentation, and sensor fusion. Despite tremendous research efforts, several issues still need to be addressed that limit the applicability of DNNs in automated driving. The bad generalization of DNNs to unseen domains is a major problem on the way to a safe, large-scale application, because manual annotation of new domains is costly, particularly for semantic segmentation. For this reason, methods are required to adapt DNNs to new domains without labeling effort. This task is termed unsupervised domain adaptation (UDA). While several different domain shifts challenge DNNs, the shift between synthetic and real data is of particular importance for automated driving, as it allows the use of simulation environments for DNN training. We present an overview of the current state of the art in this research field. We categorize and explain the different approaches for UDA. The number of considered publications is larger than any other survey on this topic. We also go far beyond the description of the UDA state-of-the-art, as we present a quantitative comparison of approaches and point out the latest trends in this field. We conduct a critical analysis of the state-of-the-art and highlight promising future research directions. With this survey, we aim to facilitate UDA research further and encourage scientists to exploit novel research directions

Thrombophilia and risk of VTE recurrence according to the age at the time of first VTE manifestation

BACKGROUND Whether screening for thrombophilia is useful for patients after a first episode of venous thromboembolism (VTE) is a controversial issue. However, the impact of thrombophilia on the risk of recurrence may vary depending on the patient's age at the time of the first VTE. PATIENTS AND METHODS Of 1221 VTE patients (42 % males) registered in the MAISTHRO (MAin-ISar-THROmbosis) registry, 261 experienced VTE recurrence during a 5-year follow-up after the discontinuation of anticoagulant therapy. RESULTS Thrombophilia was more common among patients with VTE recurrence than those without (58.6 % vs. 50.3 %; p = 0.017). Stratifying patients by the age at the time of their initial VTE, Cox proportional hazards analyses adjusted for age, sex and the presence or absence of established risk factors revealed a heterozygous prothrombin (PT) G20210A mutation (hazard ratio (HR) 2.65; 95 %-confidence interval (CI) 1.71 - 4.12; p < 0.001), homozygosity/double heterozygosity for the factor V Leiden and/or PT mutation (HR 2.35; 95 %-CI 1.09 - 5.07, p = 0.030), and an antithrombin deficiency (HR 2.12; 95 %-CI 1.12 - 4.10; p = 0.021) to predict recurrent VTE in patients aged 40 years or older, whereas lupus anticoagulants (HR 3.05; 95%-CI 1.40 - 6.66; p = 0.005) increased the risk of recurrence in younger patients. Subgroup analyses revealed an increased risk of recurrence for a heterozygous factor V Leiden mutation only in young females without hormonal treatment whereas the predictive value of a heterozygous PT mutation was restricted to males over the age of 40 years. CONCLUSIONS Our data do not support a preference of younger patients for thrombophilia testing after a first venous thromboembolic event

Mast cells as protectors of health

Mast cells (MCs), which are well known for their effector functions in T(H)2-skewed allergic and also autoimmune inflammation, have become increasingly acknowledged for their role in protection of health. It is now clear that they are also key modulators of immune responses at interface organs, such as the skin or gut. MCs can prime tissues for adequate inflammatory responses and cooperate with dendritic cells in T-cell activation. They also regulate harmful immune responses in trauma and help to successfully orchestrate pregnancy. This review focuses on the beneficial effects of MCs on tissue homeostasis and elimination of toxins or venoms. MCs can enhance pathogen clearance in many bacterial, viral, and parasitic infections, such as through Toll-like receptor 2-triggered degranulation, secretion of antimicrobial cathelicidins, neutrophil recruitment, or provision of extracellular DNA traps. The role of MCs in tumors is more ambiguous; however, encouraging new findings show they can change the tumor microenvironment toward antitumor immunity when adequately triggered. Uterine tissue remodeling by alpha-chymase (mast cell protease [MCP] 5) is crucial for successful embryo implantation. MCP-4 and the tryptase MCP-6 emerge to be protective in central nervous system trauma by reducing inflammatory damage and excessive scar formation, thereby protecting axon growth. Last but not least, proteases, such as carboxypeptidase A, released by Fc epsilon RI-activated MCs detoxify an increasing number of venoms and endogenous toxins. A better understanding of the plasticity of MCs will help improve these advantageous effects and hint at ways to cut down detrimental MC actions