An automation apparatus for dispensing groups of individually packaged products, the apparatus including a storage arrangement adapted to store a plurality of products each contained in an individual package. The storage arrangement includes a first module and a second module, where the first module is adapted to store products having a first frequency demand threshold and the second module is adapted to store products having a second frequency demand threshold that is lower than the first frequency demand threshold. The apparatus further includes a plurality of fixed first discharge assemblies adapted to move selected ones of the individual packages from the first module of the storage arrangement and a second discharge assembly comprising a moveable pick device adapted to move selected ones of the individual packages out of the second module of the storage arrangement.

An automation apparatus for dispensing groups of individually packaged products, the apparatus including a storage arrangement adapted to store a plurality of products each contained in an individual package. The storage arrangement includes a first module and a second module, where the first module is adapted to store products having a first frequency demand threshold and the second module is adapted to store products having a second frequency demand threshold that is lower than the first frequency demand threshold. The apparatus further includes a plurality of fixed first discharge assemblies adapted to move selected ones of the individual packages from the first module of the storage arrangement and a second discharge assembly comprising a moveable pick device adapted to move selected ones of the individual packages out of the second module of the storage arrangement.

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

Methods of designing a polynucleotide sequence for expressing a polypeptide-of-interest in a cell are provided. Also provided are artificial transcript sequences generated according to the present teachings. Further provided are methods of estimating the adaptiveness of a transcript sequence encoding a polypeptide-of-interest to a gene expression machinery in a cell.

Methods of designing a polynucleotide sequence for expressing a polypeptide-of-interest in a cell are provided. Also provided are artificial transcript sequences generated according to the present teachings. Further provided are methods of estimating the adaptiveness of a transcript sequence encoding a polypeptide-of-interest to a gene expression machinery in a cell.

An neural network (NN) processor includes an input feature map buffer configured to store an input feature matrix, a weight buffer configured to store a weight matrix trained in a form of a, a transform circuit configured to perform a Walsh-Hadamard transform on an input feature vector obtained from the input feature matrix and a weight vector included in the weight matrix to output a transformed input feature vector and a transformed weight vector, and an arithmetic circuit configured to perform an element-wise multiplication (EWM) on the transformed input feature vector and the transformed weight vector.

The present disclosure proposes a language generation method and apparatus. The method includes: performing encoding processing on an input sequence by using a preset encoder to generate a hidden state vector corresponding to the input sequence; in response to a granularity category of a second target segment being a phrase, decoding a first target segment vector, the hidden state vector, and a position vector corresponding to the second target segment by using N decoders to generate N second target segments; determining a loss value based on differences between respective N second target segments and a second target annotated segment; and performing parameter updating on the preset encoder, a preset classifier, and the N decoders based on the loss value to generate an updated language generation model for performing language generation.

The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

Articles of sports equipment, such as striking implements (e.g., sticks) and wearable articles (e.g., skates, helmets) for playing hockey or other activities, in which at least one of the characteristics (e.g., stiffness) of the articles of sports equipment is adaptable and can be altered during use of the sports equipment, such as by comprising an alterable component coupled to a controller, by comprising an alterable component that is autonomously alterable, and/or by an alterable component that can be altered in response to a command of a remote device.