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An efficient scheme for identifying semantic entities within data sets such as multimedia documents, scenes, signals, and so forth, is proposed in this work. Expression of semantic entities in terms of syntactic properties is modelled with appropriately defined finite automata, which also model the identification procedure. Based on the structure and properties of these automata, formal definitions of attained validity and certainty and also required complexity are defined as metrics of identification efficiency. The main contribution of the paper relies on organizing the identification and search procedure in a way that maximizes its validity for bounded complexity budgets and reversely minimizes computational complexity for a given required validity threshold. The associated optimization problem is solved by using dynamic programming. Finally, a set of experiments provides insight to the introduced theoretical framework.

Abstract Late complications of radiotherapy can include a variety of issues, such as xerostomia, dysphagia, taste changes, digestive and absorption dysfunction. These late complications can seriously impact the nutritional status and quality of life of patients following radiotherapy, and may even adversely affect tumor control and patient survival. Comprehensive nutritional management strategies should be implemented to address these late complications. For radiation-induced xerostomia, salivary stimulant drugs, saliva substitutes, and hyperbaric oxygen therapy have demonstrated efficacy in alleviating dry mouth symptoms. The management of post-radiation trismus remains challenging, with functional training, including active jaw exercises, passive motor devices, and splints, being the primary interventions. In the case of radiation-induced dysphagia, a multidisciplinary team approach is recommended, incorporating malnutrition risk assessment, personalized nutritional treatment, nursing care, rehabilitation training, and patient education. Protecting normal organs, such as the tongue and oral cavity, during radiotherapy planning can help prevent and mitigate taste changes, which can be further managed with zinc supplements, oral care, and lactoferrin supplementation. For post-radiation digestive and absorption dysfunction, a comprehensive assessment of the patient's nutritional status, site of gastrointestinal injury, and potential complications (e.g., bleeding, perforation) is crucial in guiding the selection of appropriate nutritional support, ranging from enteral nutrition as the first-line approach to supplemental or even exclusive parenteral nutrition. Surgical intervention may be necessary in some cases. By implementing these multifaceted nutritional management strategies, healthcare professionals can optimize the care of cancer patients undergoing radiotherapy and mitigate the adverse impact of late radiation complications on their overall well-being and treatment outcomes.

Background: Superior sulcus tumor is a rare non-small cell lung cancer with poor prognosis. Exploring the potential prognostic factors of patients with superior sulcus tumor and adopting individualized treatment for patients with different prognostic factors are of great significance for the prolongation of patients' lives. To figure out the prognostic factors of upper sulcus tumors, a meta-analysis was conducted.Method: We searched all the articles published until January 2020 in PubMed, Embase, and Web of Science databases, and the search strategy included the following terms, combining superior sulcus tumor and prognosis. Hazard ratio (HR) with associated confidential interval (CI) was evaluated for the purpose of investigating prognostic factors for superior sulcus tumor. STATA 16.0 was used for analysis of extracted data and assessment of publication bias.Result: Fifteen eligible studies, which had 1,009 patients with superior sulcus tumor, were included in this meta-analysis. The studies were published between 1994 and 2018, and the patient recruitment periods ranged from 1974 to 2016. The median follow-up time ranged from 18 to 95 months. The meta-analysis indicated that lower T stage (HR, 1.63; 95% CI, 1.35–1.97), lower N stage (HR, 3.08; 95% CI: 2.37–3.99), negative surgical margin (HR, 0.25; 95% CI, 0.17–0.38), and pathologic complete response (HR, 0.55; 95% CI, 0.39–0.77) were favorable prognostic factors.Conclusion: We found that T stage, N stage, surgical margin, and pathologic complete response are prognostic factors for superior sulcus tumor. To reach a better long-term survival, patients with these negative prognostic factors may need a more aggressive treatment, while more studies should be conducted to further validate these results and explore a more effective treatment.

Bias in computer vision systems can perpetuate or even amplify discrimination against certain populations. Considering that bias is often introduced by biased visual datasets, many recent research efforts focus on training fair models using such data. However, most of them heavily rely on the availability of protected attribute labels in the dataset, which limits their applicability, while label-unaware approaches, i.e., approaches operating without such labels, exhibit considerably lower performance. To overcome these limitations, this work introduces FLAC, a methodology that minimizes mutual information between the features extracted by the model and a protected attribute, without the use of attribute labels. To do that, FLAC proposes a sampling strategy that highlights underrepresented samples in the dataset, and casts the problem of learning fair representations as a probability matching problem that leverages representations extracted by a bias-capturing classifier. It is theoretically shown that FLAC can indeed lead to fair representations, that are independent of the protected attributes. FLAC surpasses the current state-of-the-art on Biased-MNIST, CelebA, and UTKFace, by 29.1%, 18.1%, and 21.9%, respectively. Additionally, FLAC exhibits 2.2% increased accuracy on ImageNet-A and up to 4.2% increased accuracy on Corrupted-Cifar10. Finally, in most experiments, FLAC even outperforms the bias label-aware state-of-the-art methods.