Towards story-based classification of movie scenes.

Humans are entertained and emotionally captivated by a good story. Artworks, such as operas, theatre plays, movies, TV series, cartoons, etc., contain implicit stories, which are conveyed visually (e.g., through scenes) and audially (e.g., via music and speech). Story theorists have explored the structure of various artworks and identified forms and paradigms that are common to most well-written stories. Further, typical story structures have been formalized in different ways and used by professional screenwriters as guidelines. Currently, computers cannot yet identify such a latent narrative structure of a movie story. Therefore, in this work, we raise the novel challenge of understanding and formulating the movie story structure and introduce the first ever story-based labeled dataset-the Flintstones Scene Dataset (FSD). The dataset consists of 1, 569 scenes taken from a manual annotation of 60 episodes of a famous cartoon series, The Flintstones, by 105 distinct annotators. The various labels assigned to each scene by different annotators are summarized by a probability vector over 10 possible story elements representing the function of each scene in the advancement of the story, such as the Climax of Act One or the Midpoint. These elements are learned from guidelines for professional script-writing. The annotated dataset is used to investigate the effectiveness of various story-related features and multi-label classification algorithms for the task of predicting the probability distribution of scene labels. We use cosine similarity and KL divergence to measure the quality of predicted distributions. The best approaches demonstrated 0.81 average similarity and 0.67 KL divergence between the predicted label vectors and the ground truth vectors based on the manual annotations. These results demonstrate the ability of machine learning approaches to detect the narrative structure in movies, which could lead to the development of story-related video analytics tools, such as automatic video summarization and recommendation systems.

Incomplete tumor volume reduction may improve cancer prognosis.

Currently, chemotherapy, immunotherapy, and radiotherapy are the standard treatment for un-resectable solid malignant tumors. New "virtual scalpel" methods, such as MRI guided focused ultrasound, can target tumor tissues for destruction without the need for a resection and its possible complications. Unfortunately, the current state of the art cannot guarantee the safe and full destruction of all the targeted tissues. The main hypothesis is that for cancer patients, even incomplete tumor volume reduction will increase the efficiency of some systemic therapies as well as of natural healing mechanisms, and thus, may improve the quality of life and the prognosis. It is conjectured that partial tumor volume destruction will reduce the releases of endocrine substances from tumor cells, thus, improving the overall systemic condition. Furthermore, it may improve the efficiency of chemotherapy and immunotherapy and possibly trigger a natural immune response. The decrease of tumor volume may also slow down the process of invasion and metastasis. It is recommended that as a standard practice of palliative care, any un-resectable tumor volume will be considered for destruction with virtual scalpel techniques such as MRI guided focused ultrasound. Furthermore, it is suggested that this process is synergetic with chemotherapy and immunotherapy.

Mammalian spore-like cells--a reservoir of spare parts for old-age?

Random exogenous environmental effects--such as radiation and virus--break the biopolymers of the cell (e.g., proteins and DNA), deteriorate its biochemical processes, and eventually cause wear-out and death. Worn-out cells are replaced through the process of cell division. However, a limit to the number of times a cell divides has been noted in all fully differentiated human cell types, as well as in other organisms. While stem cells are an exception and may continue to regenerate cells for the entire lifespan of the organism, they are susceptible too to radiation, infection, and other forms of environmental damage. Spore-like cells are small dormant simple cell-like structures which have the ability to differentiate into mature cells of the tissue from which they are isolated or into the cell types of another tissue. They seem to be present in every tissue in the body. Spore-like cells tolerate conditions that kill differentiated or partially differentiated cells, such as complete oxygen deprivation, and exposure to temperatures which are either much higher or much lower than normal body temperature. When awaken, they can proliferate more rapidly and into more types of differentiated cells than do terminally differentiated cells or stem cells. Reliability theory is a general system theory about systems failure that predicts very well the age-related mortality rates in many species. Reliability theory predicts the late-life mortality deceleration as a consequence of the exhaustion of the redundancy at extreme old-age. The main hypothesis is that the purpose of the spore-like cells is to provide redundancy (i.e., spare parts) for the differentiated and partially differentiated cells in a tissue. Since their use may be required dozens of years after their creation, they need to stay in a dormant and robust state that protects them from deterioration due to detrimental environmental effects. This hypothesis can be validated by measuring the age-related concentration of spore-like cells in various types of tissues and organs. It is predicted that the concentration of spore-like cells in various organs is highly correlated with the health of those organs, especially at old-age. It is expected that tissues with an initially large concentration of spore-like cells may live longer than tissues with an initially small concentration of spore-like cells.

Measuring progress in multirobot research with rating methods--the RoboCup example.

Rating the intelligence of artificially made systems is important for measuring progress in scientific and engineering methods. Unfortunately, there is currently no universal agreement about what is considered an intelligent system, and how to measure its intelligence. This research focus on measuring the progress in the robotic technologies deployed for the RoboCup competitions, since one of the original premises of those competitions was to advance the development of intelligent robotic systems. A method used for rating the competence of human chess players is adapted for measuring the advancement in the competence of robotic teams. The results indicate significant yearly improvements in the capabilities of the robotic teams. The same method can be used to indirectly quantify the benefits in specific technology choices.