Multi-instance active learning with online labeling for object recognition

Robots deployed in domains characterized by non-deterministic action outcomes and unforeseen changes frequently need considerable knowledge about the do-main and tasks they have to perform. Humans, however, may not have the time and expertise to provide elaborate or accurate domain knowledge, and it may be difficult for robots to obtain many labeled training samples of domain objects and events. For widespread deployment, robots thus need the ability to incrementally and auto-matically extract relevant domain knowledge from mul-timodal sensor inputs, acquiring and using human feed-back when such feedback is necessary and available. This paper describes a multiple-instance active learning algorithm for such incremental learning in the context of building models of relevant domain objects. We in-troduce the concept of bag uncertainty, enabling robots to identify the need for feedback, and to incrementally revise learned object models by associating visual cues extracted from images with verbal cues extracted from limited high-level human feedback. Images of indoor and outdoor scenes drawn from the IAPR TC-12 bench-mark dataset are used to show that our algorithm pro-vides better object recognition accuracy than a state of the art multiple-instance active learning algorithm.

Additional file 1 of Status of Iranian schools’ psycho-social environment: cultural adaptation and validation of the Persian version of the W.H.O profile to create Child-Friendly Schools

Additional file 1. W.H.O Psycho-Social Environment (PSE) Profile

Chitosan-based nanoscale delivery systems in hepatocellular carcinoma: Versatile bio-platform with theranostic application

The field of nanomedicine has provided a fresh approach to cancer treatment by addressing the limitations of current therapies and offering new perspectives on enhancing patients' prognoses and chances of survival. Chitosan (CS) is isolated from chitin that has been extensively utilized for surface modification and coating of nanocarriers to improve their biocompatibility, cytotoxicity against tumor cells, and stability. HCC is a prevalent kind of liver tumor that cannot be adequately treated with surgical resection in its advanced stages. Furthermore, the development of resistance to chemotherapy and radiotherapy has caused treatment failure. The targeted delivery of drugs and genes can be mediated by nanostructures in treatment of HCC. The current review focuses on the function of CS-based nanostructures in HCC therapy and discusses the newest advances of nanoparticle-mediated treatment of HCC. Nanostructures based on CS have the capacity to escalate the pharmacokinetic profile of both natural and synthetic drugs, thus improving the effectiveness of HCC therapy. Some experiments have displayed that CS nanoparticles can be deployed to co-deliver drugs to disrupt tumorigenesis in a synergistic way. Moreover, the cationic nature of CS makes it a favorable nanocarrier for delivery of genes and plasmids. The use of CS-based nanostructures can be harnessed for phototherapy. Additionally, the incur poration of ligands including arginylglycylaspartic acid (RGD) into CS can elevate the targeted delivery of drugs to HCC cells. Interestingly, smart CS-based nanostructures, including ROS- and pH-sensitive nanoparticles, have been designed to provide cargo release at the tumor site and enhance the potential for HCC suppression