The story of Deep Blue Sea was conceived by Australian screenwriter Duncan Kennedy after he witnessed the result of a \"horrific\" shark attack on a beach near his home. The tragedy contributed to a recurring nightmare of him \"being in a passageway with sharks that could read his mind\". This motivated him to write a spec script, while acknowledging the challenge of approaching a shark film without repeating Steven Spielberg's 1975 thriller Jaws. Although Warner Bros. bought the script in late 1994, actual development on the project did not start until two years later. When Renny Harlin was chosen to direct the film, Kennedy's screenplay, which had already been re-written by several writers at Warner Bros., was presented to Donna Powers and Wayne Powers, who turned it into the film's final script. According to Wayne, \"The movie became essentially what we wrote. The draft we were first presented by [Warner Bros.] was much more of a military espionage, high-tech action movie, grenade launchers, that kind of thing. We wanted our team to include more blue-collar types and not to have weapons to fight back, to play it more as a horror film.\"
In a 2016 retrospective, Wired editor Brian Raftery considered Deep Blue Sea \"the greatest non-Jaws shark movie of all time\" and superior to Jaume Collet-Serra's The Shallows. He remarked that, within a genre that had been dominated by Jaws, Deep Blue Sea features \"genuinely inventive\" action sequences, \"nicely rounded-out, human\" characters, and memorable death scenes. Raftery also noted that the film was among the last of its kind, describing it as \"[A]n R-rated B-movie, full of gore and chaos and smart-stupidness, but with a big-budget, big-cast sheen\", in a similar way to Paul Verhoeven's Total Recall and Starship Troopers, Roland Emmerich's Stargate, and Luc Besson's The Fifth Element. Samuel L. Jackson's surprising death scene in the film appears on some lists of best film deaths of all time.
Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too many variables to define anomalies, and the human annotation for a large collection of abnormal data labeled at the class-level is very labor-intensive. In this paper, we propose to detect abnormal operation sounds or outliers in a very complex machine along with reducing the data-driven annotation cost. The architecture of the proposed model is based on an auto-encoder, and it uses the residual error, which stands for its reconstruction quality, to identify the anomaly. We assess our model using Surface-Mounted Device (SMD) machine sound, which is very complex, as experimental data, and state-of-the-art performance is successfully achieved for anomaly detection.
The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field. 1e1e36bf2d