Abstract: The present disclosure relates generally to terpene glycosides, such as certain such compounds extracted from Stevia rebaudiana Bertoni, Rubus suavissimus, or Siraitia grosvenorii. The disclosure also provides for the use of such compounds as food ingredients, flavors, and sweeteners, and related methods. The disclosure also provides ingestible compositions comprising such compounds, as well as processes for extracting such compounds selectively from certain plant sources.

Abstract: A method and system for generating a three-dimensional (3D) virtual scene are disclosed. The method includes: identifying a two-dimensional (2D) object in a 2D picture and the position of the 2D object in the 2D picture; obtaining the three-dimensional model of the 3D object corresponding to the 2D object; calculating the corresponding position of the 3D object corresponding to the 2D object in the horizontal plane of the 3D scene according to the position of the 2D object in the picture; and simulating the falling of the model of the 3D object onto the 3D scene from a predetermined height above the 3D scene, wherein the position of the landing point the model of the 3D object in the horizontal plane is the corresponding position of the 3D object in the horizontal plane of the 3D scene.

Abstract: A method and system for generating a three-dimensional (3D) virtual scene are disclosed. The method includes: identifying a two-dimensional (2D) object in a 2D picture and the position of the 2D object in the 2D picture; obtaining the three-dimensional model of the 3D object corresponding to the 2D object; calculating the corresponding position of the 3D object corresponding to the 2D object in the horizontal plane of the 3D scene according to the position of the 2D object in the picture; and simulating the falling of the model of the 3D object onto the 3D scene from a predetermined height above the 3D scene, wherein the position of the landing point the model of the 3D object in the horizontal plane is the corresponding position of the 3D object in the horizontal plane of the 3D scene.

Abstract: Described herein are an extract from lettuce (Lactuca sativa), in particular of stalk lettuce (Lactuca sativa var. angustana), including 2-acetyl-1-pyrroline and, optionally, precursors of 2-acetyl-1-pyrroline as well as flavoring compositions including such an extract and a method for the preparation of such an extract. Also described herein are the use of such an extract as a taste or flavor ingredient in a consumer product and a method of enhancing, improving, modifying the taste or flavor of a consumer product by making use of such an extract.

Abstract: A method and system for generating a three-dimensional (3D) virtual scene are disclosed. The method includes: identifying a two-dimensional (2D) object in a 2D picture and the position of the 2D object in the 2D picture; obtaining the three-dimensional model of the 3D object corresponding to the 2D object; calculating the corresponding position of the 3D object corresponding to the 2D object in the horizontal plane of the 3D scene according to the position of the 2D object in the picture; and simulating the falling of the model of the 3D object onto the 3D scene from a predetermined height above the 3D scene, wherein the position of the landing point the model of the 3D object in the horizontal plane is the corresponding position of the 3D object in the horizontal plane of the 3D scene.

Abstract: The present disclosure relates generally to terpene glycosides, such as certain such compounds extracted from Stevia rebaudiana Bertoni, Rubus suavissimus, or Siraitia grosvenorii. The disclosure also provides for the use of such compounds as food ingredients, flavors, and sweeteners, and related methods. The disclosure also provides ingestible compositions comprising such compounds, as well as processes for extracting such compounds selectively from certain plant sources.

Abstract: A method and system for generating a three-dimensional (3D) virtual scene are disclosed. The method includes: identifying a two-dimensional (2D) object in a 2D picture and the position of the 2D object in the 2D picture; obtaining the three-dimensional model of the 3D object corresponding to the 2D object; calculating the corresponding position of the 3D object corresponding to the 2D object in the horizontal plane of the 3D scene according to the position of the 2D object in the picture; and simulating the falling of the model of the 3D object onto the 3D scene from a predetermined height above the 3D scene, wherein the position of the landing point the model of the 3D object in the horizontal plane is the corresponding position of the 3D object in the horizontal plane of the 3D scene.

Abstract: A system for training and deploying an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a memory storing machine readable instructions. The system may also comprise a processor to execute the machine readable instructions to receive a request via an artificial conversational entity. The processor may also transmit a response to the request based on a dialog tree generated from at least a model-based action generator and a memory-based action generator. The processor may further provide a training option to a user in the event the response is suboptimal. The processor may additionally receive a selection from the user via the training option. The selection may be associated with an optimal response.

Abstract: Systems and devices to perform knowledge-driven dynamic conversations and select content within automated agents such as chatbots and virtual assistants are disclosed. In an example, operations to facilitate a knowledge-based conversation session with a human user using an automated agent include: receiving a conversational input regarding a support issue; analyzing the conversational input to determine an intent and applicable entity properties associated with the intent; performing a multi-turn conversation to identify a solution using the intent and the applicable entity properties, by exchanging iterative questions and answers between the automated agent and the user to dynamically recalculate applicability of the solution to the support issue; and outputting information associated with the identified solution.

Abstract: Technologies described herein relate to receiving data from a social networking application, wherein the data is generated by a first individual, and executing an entity extraction algorithm to extract at least one entity from the received data. A hyperlink is displayed on a computing device utilized by a second individual, wherein the hyperlink is based at least in part upon the at least one entity, and the hyperlink is configured to direct the first individual to a search engine and is further configured to cause the search engine to execute a search that pertains to the at least one entity.

Abstract: A system for training and deploying an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a memory storing machine readable instructions. The system may also comprise a processor to execute the machine readable instructions to receive a request via an artificial conversational entity. The processor may also transmit a response to the request based on a dialog tree generated from at least a model-based action generator and a memory-based action generator. The processor may further provide a training option to a user in the event the response is suboptimal. The processor may additionally receive a selection from the user via the training option. The selection may be associated with an optimal response.

Abstract: Systems and devices to perform knowledge-driven dynamic conversations and select content within automated agents such as chatbots and virtual assistants are disclosed. In an example, operations to facilitate a knowledge-based conversation session with a human user using an automated agent include: receiving a conversational input regarding a support issue; analyzing the conversational input to determine an intent and applicable entity properties associated with the intent; performing a multi-turn conversation to identify a solution using the intent and the applicable entity properties, by exchanging iterative questions and answers between the automated agent and the user to dynamically recalculate applicability of the solution to the support issue; and outputting information associated with the identified solution.

Abstract: Systems and devices to perform content authoring and establish conversation models for automated agents such as chatbots and virtual assistants are disclosed. In an example, operations for content authoring, to produce a conversation model, include: identifying respective intents from conversation segments in an unstructured data source; generating a knowledge graph of the conversation model to organize the identified intents; linking the intents in the knowledge graph to properties of the respective conversations, for properties used to guide a subject conversation with the conversation model; and outputting the conversation model, to be usable with the automated agent to conduct the subject conversation with a human user.

Abstract: Various technologies pertaining to exploratory suggestions are described herein. A computer-implemented graph is constructed, where the graph includes nodes that are representative of aspects and edges that are representative of associations between aspects. An aspect is representative of a sub-topic of a topic or a sub-task of a task. The computer-implemented graph is learned based upon content of search logs, and is used to output exploratory suggestions, where a user is exploring a topic or attempting to complete a multi-step task.

Abstract: Managing software performance debugging based on a distributed VM system is provided. In response to determining a debugging state of a software system running on a VM, a timing of a system clock of the VM is controlled. A data packet sent to the VM from another VM is intercepted, and an added system time and reference time that indicate when the packet was sent by the other VM is extracted from the packet. Based on the system and reference times, as well as a reference time of when the packet is intercepted, a timing at which the packet is expected to be received by the VM is calculated. The packet is forwarded to the VM as a function of a comparison of the timing at which the packet is expected to be received and a system time of the VM when the packet is intercepted.

Abstract: A system is described herein that includes a preference deriver component that receives a predefined preference rule that indicates a hierarchy pertaining to entities belonging to a domain, wherein each of the entities has attributes and values for such attributes corresponding thereto, and wherein the preference deriver component outputs preferences between various subsets of entities based at least in part upon the preference rule. The system also includes a learning component that learns a computer-implemented ranker component that is configured to rank the entities belonging to the domain, wherein the learning component learns the computer-implemented ranker based at least in part upon the preferences between the various subsets of the entities output by the preference deriver component.

Abstract: An electronic device including a casing, a printed circuit board, a contact spring and a connector is provided. The printed circuit board is disposed in the casing, and the contact spring is electrically connected to and located on the printed circuit board. The connector is movably disposed at the casing, and a part of the connector is protruded from the casing. While the connector is pressed by external force, the connector is moved to contact the contact spring so as to conduct with the printed circuit board.

Abstract: Technologies pertaining to automatically identifying sets of query attribute values that are highly correlative with user dissatisfaction with a search engine are described. Dissatisfied queries are automatically identified through analysis of search logs, wherein a dissatisfied query is a query submitted to a search engine by a user, wherein the user was dissatisfied with search results provided by the search engine responsive to receipt of the query. Sets of query attribute values that are highly correlated with dissatisfied queries, and thus user dissatisfaction, are automatically identified based at least in part upon the identifying of the dissatisfied queries. Subsequent to identifying a set of query attribute values, a segment-specific ranker is learned that is configured to rank search results responsive to receipt of a query with the set of query attribute values, wherein the segment-specific ranker outperforms a general purpose ranker for queries having the set of query attribute values.

Abstract: An auto focus device comprises a focus panel and a focus controller. The focus panel comprises liquid crystal between a first light-transmissive conductive film and a second light-transmissive conductive film, and the focus controller is configured to apply a voltage between the two light-transmissive conductive films at the position of at least one pixel, so that the liquid crystal at the position will have an expected focal length. An auto focus method comprises: acquiring information on eyesight status of a user; acquiring information on posture of the user; calculating expected focal length of the liquid crystal between the two light-transmissive conductive films at the position of at least one pixel according to the acquired user information; selecting a voltage to be applied between the two light-transmissive conductive films at the position according to the expected focal length; and applying the voltage between the two light-transmissive conductive films at the position.

Abstract: The assistive hearing device implementations described herein assist hearing impaired users of the device by using automated speech transcription to generate text representing speech received in audio signals which can then be read in a synthesized voice tailored to overcome a user's hearing deficiencies. A speech recognition engine recognizes speech in received audio and converts the speech of the received audio to text. Once the speech is converted to text, a text-to-speech engine can convert the text to synthesized speech that can be enhanced and output in a voice that compensates for the hearing loss profiles of a user of the assistive hearing device. By transcribing received speech into text the assistive hearing device implementations described herein eliminate background noise from the audio signal. By converting the transcribed text into a synthesized voice that is easier to understand to hearing impaired persons, their hearing deficiencies can be remedied.