Abstract: The computer system applies machine learning techniques to train a computational model using data representing researched items and their known properties. The computer system applies the trained computational model to data representing the potential candidate items to predict whether such items have such properties. The trained computational model outputs one or more predictions about whether the potential candidate items are likely to have a property from among the plurality of types of properties that the computational model is trained to predict. The computer system allows multiple machine learning experiments to be defined, and then allows predictions from those multiple machine learning experiments to be queried, including accessing aggregate statistics for those predictions. In some implementations, a machine learning experiment can specify a computational model that is an ensemble of multiple models.

Abstract: Described herein are systems, methods, and other techniques for training a generative adversarial network (GAN) to perform an image-to-image transformation for recognizing text. A pair of training images are provided to the GAN. The pair of training images include a training image containing a set of characters in handwritten form and a reference training image containing the set of characters in machine-recognizable form. The GAN includes a generator and a discriminator. The generated image is generated using the generator based on the training image. Update data is generated using the discriminator based on the generated image and the reference training image. The GAN is trained by modifying one or both of the generator and the discriminator using the update data.

Abstract: Described herein are systems, methods, and other techniques for training a generative adversarial network (GAN) to perform an image-to-image transformation for recognizing text. A pair of training images are provided to the GAN. The pair of training images include a training image containing a set of characters in handwritten form and a reference training image containing the set of characters in machine-recognizable form. The GAN includes a generator and a discriminator. The generated image is generated using the generator based on the training image. Update data is generated using the discriminator based on the generated image and the reference training image. The GAN is trained by modifying one or both of the generator and the discriminator using the update data.

Abstract: A fixative for biological tissue made up of polymerized carbon nanotubes encapsulating osmium nanoparticles and its method of synthesis are disclosed. Carbon nanotubes are first oxidized. Next, the oxidized carbon nanotubes and monohydrated citric acid are mixed to synthesize carbon nanotubes grafted with poly(citric acid). The carbon nanotubes grafted with poly(citric acid) are then mixed with an osmium source to synthesize carbon nanotubes grafted with poly(citric acid) encapsulating osmium nanoparticles. The nano-fixative of this application has been shown to improve fixation of biological tissue relative to well-known fixatives.

Abstract: A fixative for biological tissue made up of polymerized carbon nanotubes encapsulating osmium nanoparticles and its method of synthesis are disclosed. Carbon nanotubes are first oxidized. Next, the oxidized carbon nanotubes and monohydrated citric acid are mixed to synthesize carbon nanotubes grafted with poly(citric acid). The carbon nanotubes grafted with poly(citric acid) are then mixed with an osmium source to synthesize carbon nanotubes grafted with poly(citric acid) encapsulating osmium nanoparticles. The nano-fixative of this application has been shown to improve fixation of biological tissue relative to well-known fixatives.