Abstract: The present disclosure provides a metal wire and a method for manufacturing the same. The method for manufacturing the metal wire includes: forming a metal bar on a substrate; forming a mask above the metal bar, a width of the mask being smaller than a width of the metal bar, and an orthographic projection of the mask on the substrate is within an orthographic projection of the metal bar on the substrate; and wet etching the metal bar to a saturation state under a protection of the mask to form a metal wire, a width of the metal wire being smaller than the width of the mask. The above method can form the metal wire with a high thickness and a narrow line width.

Abstract: A single-stage enthalpy enhancing rotary compressor and an air conditioner having same. The single-stage enthalpy enhancing rotary compressor includes: at least one single-stage cylinder, a rotator, an upper flange, and a lower flange. The rotator is arranged inside the cylinder and is rotatable, a compression chamber is formed between the rotator and an inner peripheral wall of the cylinder, a vapor injection opening is defined in at least one of the upper flange the lower flange, and the vapor injection opening is configured to supply gas outside the compressor to the compression chamber directly. According to the present disclosure, two-stage compression is realized without adding an extra cylinder, thereby effectively enhancing a circulation of refrigerant, improving cooling performance of the air conditioner under high environmental temperatures.

Abstract: A method for training a dialogue learning model includes presenting, via a user interface of a computing device, an utterance and a list of actions based on the utterance. A selection of an action from the list of actions is received via the user interface. A designated span of the utterance is received via the user interface. The selected action and the designated span of the utterance is provided to a computing system for training the dialogue learning model.

Abstract: An automated natural dialogue system provides a combination of structure and flexibility to allow for ease of annotation of dialogues as well as learning and expanding the capabilities of the dialogue system based on natural language interactions.

Abstract: A decoding method of low-density parity-check (LDPC) codes based on partial average residual belief propagation includes the following steps: S1: calculating a size of a cluster ? in a protograph based on a code length m and a code rate of a target codeword; S2: pre-computing an edge residual rci?vj corresponding to each edge from a variable node to a check node in a check matrix H; S3: calculating, based on ?, a partial average residual (PAR) value corresponding to each cluster in the check matrix H; S4: sorting m/? clusters in descending order of corresponding PAR values, and updating an edge with a largest edge residual in each cluster; S5: updating edge information mci?vi from a check node ci to a variable node vj, and then updating a log-likelihood ratio (LLR) value L(vj) of the variable node vj; and S6: after the updating, making a decoding decision.

Abstract: A decoding method of low-density parity-check (LDPC) codes based on partial average residual belief propagation includes the following steps: S1: calculating a size of a cluster ? in a protograph based on a code length m and a code rate of a target codeword; S2: pre-computing an edge residual rci?vj corresponding to each edge from a variable node to a check node in a check matrix H; S3: calculating, based on ?, a partial average residual (PAR) value corresponding to each cluster in the check matrix H; S4: sorting m/? clusters in descending order of corresponding PAR values, and updating an edge with a largest edge residual in each cluster; S5: updating edge information mc?vi from a check node ci to a variable node vj, and then updating a log-likelihood ratio (LLR) value L(vj) of the variable node vj; and S6: after the updating, making a decoding decision.

Abstract: A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.

Abstract: An automated natural dialogue system provides a combination of structure and flexibility to allow for ease of annotation of dialogues as well as learning and expanding the capabilities of the dialogue system based on natural language interactions.

Abstract: A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.

Abstract: A method for generating training data for training a natural language processing system comprises loading, into a computer memory, a computer-readable transcript representing an ordered sequence of one or more dialogue events. The method further comprises acquiring a computer-readable command describing an exemplary ordered subsequence of one or more dialogue events from the computer-readable transcript. The method further comprises re-parametrizing the computer-readable command with an alternative semantic parameter. The method further comprises generating an alternative ordered subsequence of one or more dialogue events based on the re-parametrized computer-readable command. The method further comprises outputting, to a data store, an alternative computer-readable transcript including the alternative ordered subsequence of one or more dialogue events, the alternative computer-readable transcript having a predetermined format usable to train the computerized assistant.

Abstract: An automated natural dialogue system provides a combination of structure and flexibility to allow for ease of annotation of dialogues as well as learning and expanding the capabilities of the dialogue system based on natural language interactions.

Abstract: A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.

Abstract: A computing device includes a display configured to present a graphical user interface. The graphical user interface includes a transcript portion configured to display an unannotated transcript representing an ordered sequence of one or more dialogue events involving a client and a computerized assistant, at least one of the dialogue events taking the form of an example client utterance, and an annotation portion configured to display a hierarchical menu including a plurality of candidate utterance annotations. An utterance annotation machine is configured to receive one or more computer inputs selecting, for each of one or more response parameters in the example client utterance, utterance annotations from the hierarchical menu that collectively define a machine-readable interpretation of the example client utterance. An annotated utterance having a predetermined format usable to train the computerized assistant is output to a data store based on the example client utterance.

Abstract: A data collection system is based on a general set of dialogue acts which are derived from a database schema. Crowd workers perform two types of tasks: (i) identification of sensical dialogue paths and (ii) performing context-dependent paraphrasing of these dialogue paths into real dialogues. The end output of the system is a set of training examples of real dialogues which have been annotated with their logical forms. This data can be used to train all three components of the dialogue system: (i) the semantic parser for understanding context-dependent utterances, (ii) the dialogue policy for generating new dialogue acts given the current state, and (iii) the generation system for both deciding what to say and how to render it in natural language.

Abstract: A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.

Abstract: A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.

Abstract: A computing device includes a display configured to present a graphical user interface. The graphical user interface includes a transcript portion configured to display an unannotated transcript representing an ordered sequence of one or more dialogue events involving a client and a computerized assistant, at least one of the dialogue events taking the form of an example client utterance, and an annotation portion configured to display a hierarchical menu including a plurality of candidate utterance annotations. An utterance annotation machine is configured to receive one or more computer inputs selecting, for each of one or more response parameters in the example client utterance, utterance annotations from the hierarchical menu that collectively define a machine-readable interpretation of the example client utterance.

Abstract: A method for generating training data for training a natural language processing system comprises loading, into a computer memory, a computer-readable transcript representing an ordered sequence of one or more dialogue events. The method further comprises acquiring a computer-readable command describing an exemplary ordered subsequence of one or more dialogue events from the computer-readable transcript. The method further comprises re-parametrizing the computer-readable command with an alternative semantic parameter. The method further comprises generating an alternative ordered subsequence of one or more dialogue events based on the re-parametrized computer-readable command. The method further comprises outputting, to a data store, an alternative computer-readable transcript including the alternative ordered subsequence of one or more dialogue events, the alternative computer-readable transcript having a predetermined format usable to train the computerized assistant.

Abstract: An automated natural dialogue system provides a combination of structure and flexibility to allow for ease of annotation of dialogues as well as learning and expanding the capabilities of the dialogue system based on natural language interactions.

Abstract: A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.