Abstract: This specification is generally directed to techniques for robust natural language (NL) based control of computer applications. In many implementations, the NL control is at least selectively interactive in that the user feedback input is solicited, and received, in resolving action(s), resolving action set(s), generating domain specific knowledge, and/or in providing feedback on implemented action set(s). The user feedback input can be utilized in further training of machine learning model(s) utilized in the NL based control of the computer applications.

Abstract: Implementations are described herein for aggregating information responsive to a query from multiple different data feed services using machine learning. In various implementations, NLP may be performed on a natural language input comprising a query for information to generate a data feed-agnostic aggregator embedding (FAAE). A plurality of data feed services may be selected, each having its own data feed service action space that includes actions that are performable to access data via the data feed service. The FAAE may be processed based on domain-specific machine learning models corresponding to the selected data feed services. Each domain-specific machine learning model may translate between a respective data feed service action space and a data feed-agnostic semantic embedding space. Using these models, action(s) may be selected from the data feed service action spaces and performed to aggregate, from the plurality of data feed services, data that is responsive to the query.

Abstract: Disclosed implementations relate to automatically generating and providing guidance for navigating HCIs to carry out semantically equivalent/similar computing tasks across different computer applications. In various implementations, a domain of a first computer application that is operable using a first HCI may be used to select a domain model that translates between an action space of the first computer application and another space. Based on the selected domain model, a domain-agnostic action embedding—representing actions performed previously using a second HCI of a second computer application to perform a semantic task—may be processed to generate probability distribution(s) over actions in the action space of the first computer application. Based on the probability distribution(s), actions may be identified that are performable using the first computer application—these actions may be used to generate guidance for navigating the first HCI to perform the semantic task.

Abstract: The technology enables locating asset tracking tags based on one or more beacon signals from at least one anchor beacon. Each of the beacon signals including anchor beacon identification information and being associated with a received signal strength upon receipt at a reader device. The anchor beacon identification information being associated with a physical location of the anchor beacon. A position of the reader device is estimated according to the received signal strength of the one or more beacon signals and the physical location of the at least one anchor beacon from the anchor beacon identification information. One or more signals from an asset tracking tag are detected by the reader device. A location of the asset tracking tag is identified based on the estimated position of the reader device and signal strength information for each of the one or more detected signals from the asset tracking tag.

Abstract: The present disclosure relates generally to infrastructure for queryable supergraph subset representations. Briefly, in at least one implementations, an apparatus may comprise at least one processor of at least one computing device to obtain a source graph schema, obtain an input from a user, wherein the input from the user to at least specify one or more filters, generate one or more subset representations of the source graph schema based at least in part on the specified one or more filters, and provide access to the one or more subset representations of the source graph schema for one or more particular entities and deny access to one or more aspects of the source graph schema absent from the one or more subset representations for the one or more particular entities.

Abstract: As opposed to a rigid approach, implementations disclosed herein utilize a flexible approach in automatically determining an action set to utilize in attempting performance of a task that is requested by natural language input of a user. The approach is flexible at least in that embedding technique(s) and/or action model(s), that are utilized in generating action set(s) from which the action set to utilize is determined, are at least selectively varied. Put another way, implementations leverage a framework via which different embedding technique(s) and/or different action model(s) can at least selectively be utilized in generating different candidate action sets for given NL input of a user. Further, one of those action sets can be selected for actual use in attempting real-world performance of a given task reflected by the given NL input. The selection can be based on a suitability metric for the selected action set and/or other considerations.

Abstract: Disclosed implementations relate to automating semantically-similar computing tasks across multiple contexts. In various implementations, an initial natural language input and a first plurality of actions performed using a first computer application may be used to generate a first task embedding and a first action embedding in action embedding space. An association between the first task embedding and first action embedding may be stored. Later, subsequent natural language input may be used to generate a second task embedding that is then matched to the first task embedding. Based on the stored association, the first action embedding may be identified and processed using a selected domain model to select actions to be performed using a second computer application. The selected domain model may be trained to translate between an action space of the second computer application and the action embedding space.

Abstract: The invention relates to the field of antibodies. In particular it relates to the field of therapeutic (human) antibodies for the treatment of ErbB-2/ErbB-3 positive cells. More in particular it relates to treating of cells comprising an NRG1 fusion gene comprising at least a portion of the NRG1-gene fused to a sequence from a different chromosomal location.

Abstract: Techniques are described herein for translating source code in one programming language to source code in another programming language using machine learning. A method includes: receiving first source code in a first higher-level programming language; processing the first source code, or an intermediate representation thereof, using a sequence-to-sequence neural network model to generate a sequence of outputs, each including a probability distribution; generating second source code in a second higher-level programming language by, for each output in the sequence of outputs: determining a highest probability in the probability distribution associated with the output; in response to the highest probability exceeding a first threshold, generating a predicted portion of the second source code based on a token that corresponds to the highest probability; and in response to the highest probability not exceeding the first threshold, generating a placeholder; and outputting the second source code.

Abstract: Implementations are directed to improving network anti-fragility. In some aspects, a method includes receiving parameter data from a network of nodes, the parameter data comprising attributes, policies, and action spaces for each node in the network of nodes; configuring one or more interruptive events on one or more nodes included in the network of nodes; determining a first action of each node in the network of nodes in response to the one or more interruptive events; determining a first performance metric, for each node, that corresponds to the first action, wherein the first performance matric is determined based on at least a first reward value associated with the first action; continuously updating the first action in an iterative process to obtain a final action, wherein a performance metric corresponding to the final action satisfies a performance threshold, and transmitting the final action for each node to the network of nodes.

Abstract: Using a natural language (NL) latent presentation in the automated conversion of source code from a base programming language (e.g., C++) to a target programming language (e.g., Python). A base-to-NL model can be used to generate an NL latent representation by processing a base source code snippet in the base programming language. Further, an NL-to-target model can be used to generate a target source code snippet in the target programming language (that is functionally equivalent to the base source code snippet), by processing the NL latent representation. In some implementations, output(s) from the NL-to-target model indicate canonical representation(s) of variables, and in generating the target source code snippet, technique(s) are used to match those canonical representation(s) to variable(s) of the base source code snippet. In some implementations, multiple candidate target source code snippets are generated, and a subset (e.g., one) is selected based on evaluation(s).

Abstract: The technology enables locating asset tracking tags based on a ramped sequence of signals from one or more beacon tracking tags. The sequence includes at least one minimum power signal and at least one maximum power signal. Each signal in the sequence has a tag identifier and an initial signal strength value. Each beacon signal in the ramped sequence is associated with the time at which that beacon signal was received by a reader. Each beacon signal is also associated with a received signal strength at reception. A location of the beacon tracking tag is estimated according to the signals in the sequence based on the difference between the initial and received signal strengths. A position of the reader device is identified based on the beacon tag's location. An asset tracking tag location is identified based on the reader's location and packets received by the reader from the asset tag.

Abstract: Autonomous control of a subject vehicle including a longitudinal motion control system includes determining states of parameters associated with a trajectory for the subject vehicle and parameters associated with a control reference determined for the subject vehicle. A range control routine is executed to determine a first parameter associated with a range control command based upon the states of the plurality of parameters, and a speed control routine is executed to determine a second parameter associated with a speed control command based upon the states of the plurality of parameters. An arbitration routine is executed to evaluate the range control command and the speed control command, and operation of the subject vehicle is controlled to achieve a desired longitudinal state, wherein the desired longitudinal state is associated with a minimum of the range control command and the speed control command.

Abstract: Implementations are described herein for using machine learning to perform various tasks related to migrating source code based on relatively few (“few shots”) demonstrations. In various implementations, an autoregressive language model may be conditioned based on demonstration tuple(s). In some implementations, a demonstration tuple may include a pre-migration version of a first source code snippet and a post-migration version of the first source code snippet. In other implementations, demonstration tuples may include other data, such as intermediate forms (e.g., natural language descriptions or pseudocode), input-output pairs demonstrating intended behavior, etc. The autoregressive language model may be trained on corpora of source code and natural language documentation on the subject of computer programming.

Abstract: Methods and systems including receiving a plurality of shipping bids from a plurality of shipping entities, each entity having goods to ship from locations to destinations, wherein each bid represents an option to ship goods at a shipping price, and wherein each bid comprises a plurality of shipping parameters; receiving a plurality of carrier bids from a plurality of carrier entities, each entity transporting the goods, wherein each bid represents an option to transport the goods at a price, and wherein each bid comprises a plurality of carrier parameters; performing a matching process to generate a plurality of pair-wise partial matches, wherein each match associates a shipping and carrier bid at a modified price, wherein the modified price is based on a deviation between the parameters; providing information representing the matches to the shipping and carrier entities; and generating training data representing which matches were exercised.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automatic generation of a supply chain simulation. The methods, systems, and apparatus include actions of obtaining supply chain data of a supply chain, generating a supply chain network graph that represents relationships between locations indicated by the supply chain data, determining classifications of the locations indicated by the supply chain data, determining agent rule models based on the supply chain data, and generating a supply chain simulation based on the supply chain network graph, the classifications of the locations, and the agent rule models.

Abstract: A container for a plurality of articles comprising a main body, a hollow member fixedly coupled to an end of the main body and a cup member movably coupled to the hollow member. When the cup member is in an open position the cup member and the hollow member are spaced defining an opening. A slider is movably coupled to the cup member. The slider has a spring, such that when the slider is moved relative to the cup member in a radial direction the slider carries an article towards the opening and the spring on the slider is flexed. After dispensing, the spring gradually relaxes, resetting the slider.

Abstract: Implementations are described herein for using machine learning to perform various tasks related to migrating source code based on relatively few (“few shots”) demonstrations. In various implementations, an autoregressive language model may be conditioned based on demonstration tuple(s). In some implementations, a demonstration tuple may include a pre-migration version of a first source code snippet and a post-migration version of the first source code snippet. In other implementations, demonstration tuples may include other data, such as intermediate forms (e.g., natural language descriptions or pseudocode), input-output pairs demonstrating intended behavior, etc. The autoregressive language model may be trained on corpora of source code and natural language documentation on the subject of computer programming.

Abstract: In one aspect, there is provided a method performed by one or more data processing apparatus for selecting actions to be performed by an agent interacting with an environment, the method including, at each of multiple time steps, receiving an observation characterizing a current state of the environment at the time step, providing an input including the observation to an action selection neural network having a brain emulation sub-network with an architecture that is based on synaptic connectivity between biological neurons in a brain of a biological organism, processing the input including the observation characterizing the current state of the environment at the time step using the action selection neural network having the brain emulation sub-network to generate an action selection output, and selecting an action to be performed by the agent at the time step based on the action selection output.

Abstract: Implementations are described herein for formulating natural language descriptions based on temporal sequences of digital images. In various implementations, a natural language input may be analyzed. Based on the analysis, a semantic scope to be imposed on a natural language description that is to be formulated based on a temporal sequence of digital images may be determined. The temporal sequence of digital images may be processed based on one or more machine learning models to identify one or more candidate features that fall within the semantic scope. One or more other features that fall outside of the semantic scope may be disregarded. The natural language description may be formulated to describe one or more of the candidate features.