Abstract: The present composition for maintaining or improving the quality of processed meat comprises on a dry matter basis: between 20 and 60% w/w acid equivalent of an organic acid component selected from the group of acetate, lactate and combinations thereof; between 0.05 and 5% w/w acid equivalent of nitrite; between 0.5 and 5% w/w acid equivalent of ascorbate; and between 5 and 50% w/w of a vegetal fiber selected from the group of microfibrillated cellulose fiber, inulin and combinations thereof; wherein the composition when diluted with distilled water of 20° C. to a dry matter content of 10% w/w has a pH in the range of 5.0 to 9.0. This composition can replace conventional stabilizers, acid regulators and anti-oxidants without deteriorating the quality of the processed meat. Furthermore, this composition can be composed of “label friendly” ingredients, with a more attractive consumer perception. The invention also relates to a method for the preparation of the aforementioned meat treatment composition.

Abstract: Disclosed herein is a neuromorphic percolating network (100). The network comprises a substrate (102) having at least two electrical contacts (104A, 104B); a plurality of nanoparticles (106, 108, 100) distributed on the substrate (102), at least some of the nanoparticles positioned between at least two of the at least two electrical contacts, the surface coverage of the nanoparticles within a tolerance of a percolation threshold; and at least one memristive element (116) located at least partially in a gap between nanoparticles, or groups of nanoparticles, that are not otherwise directly connected to each other, the memristive element(s) at least partially providing at least one persistent pathway (118,120) of increased activity. Disclosed herein are methods for fabricating a neuromorphic percolating network or component thereof, and neuromorphic percolating networks formed by such methods.

Abstract: The present invention extracts and then analyses data based on a set of communication between an enterprise and a user associated with the enterprise. A system for extracting and analyzing the data comprising: a database and a processor communicatively coupled with the database and an Output Unit. The processor is configured to categorize data present in the database after extraction, associate the data with a set of parameters, and send the associated data to the output unit. The database includes user metadata, interaction data, and interaction metadata and the processor comprises a Data Acquisition Unit, a Data Categorization Unit, and a Model Processing Unit. The Model Processing Unit comprises of a Habit Formation Unit and a Recommendation Unit. Further, a method for extracting and analyzing data of a user of an enterprise over a pre-defined period of time has also been discussed.

Abstract: Systems and methods for batching operations in a virtualization environment. A method embodiment operates over a plurality of virtual machines in the virtualization environment. A user interface is used to select two or more virtual machines that are to be subjected to the same batch actions. A method step then generates at least one batch request to be performed over the two or more selected virtual machines. In forming the batch request, the states of the individual virtual machines are analyzed to determine one or more entity-specific operations that apply to the virtual machines and/or to constituent entities of the virtual machines. Once the state-specific and entity-specific operations have been determined, an entity management protocol initiates execution of the one or more entity-specific operations over the individual ones of the two or more selected virtual machines.

Abstract: Systems and techniques are described herein for updating a machine learning model on edge servers. Local parameters of the machine learning model are updated at a plurality of edge servers using fresh data on the edge servers, rather than waiting for the data to reach a global server to update the machine learning model. Hence, latency is significantly reduced, making the systems and techniques described herein suitable for real-time services that support streaming data. Moreover, by updating global parameters of the machine learning model at a global server in a deterministic manner based on parameter updates from the edge servers, rather than by including randomization steps, global parameters of the converge quickly to their optimal values. The global parameters are sent from the global server to the plurality of edge servers at each iteration, thereby synchronizing the machine learning model on the edge servers.

Abstract: A service provider system may provision virtualized computing resources to implement a virtual desktop instance. An interactivity agent installed on the virtual desktop instance may collect data representing interactions between a user and various input devices (e.g. a keyboard or mouse) that are associated with the execution of various desktop applications, and may provide the interactivity data to a desktop application preferences service implemented in the service provider system. The desktop application preferences service may, based on the interactivity data, characterize the usage of the desktop applications and their targets (e.g., documents) to determine which applications and targets are likely to be used during subsequent virtual desktop sessions (e.g., those used most or most recently).

Abstract: A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.

Abstract: Provided is a method, performed by an electronic device, of performing an operation based on bending, the method including: sensing bending that deforms a shape of the electronic device; determining a first region, from which the bending is sensed, from among regions of the electronic device; selecting an object indicated by the first region from at least one object displayed on the electronic device; and performing an operation on the selected object.

Abstract: Contextual spelling methods and systems are provided that utilize natural language processing and n-gram frequencies to group documents into logical groups and to provide spelling correction suggestions. For example, a contextual spelling correction system may receive a set of documents, group the documents into separate logical groups, generate dictionaries associated with the logical groups, receive a user input, determine scores for potential spelling correction suggestions regarding the user input, and provide spelling correction suggestions based at least partly on the dictionaries associated with the logical groups.

Abstract: A computer-implemented technique receives an input expression that a user submits with an intent to accomplish some objective. The technique then uses a machine-trained intent encoder component to map the input expression into an input expression intent vector (IEIV). The IEIV corresponds to a distributed representation of the intent associated with the input expression, within a vector intent vector space. The technique then leverages the intent vector to facilitate some downstream application task, such as the retrieval of information. Some application tasks also use a neighbor search component to find expressions that express an intent similar to that of the input expression. A training system trains the intent encoder component based on the nexus between queries and user clicks, as recorded in a search engine's search log.

Abstract: A system is provided that includes a memory system storing a plurality of executable instructions. The system also includes a processing system operably coupled to the memory system. The executable instructions when executed by the processing system cause the system to access a plurality of building structural data, ductwork layout data, and fire suppression system layout data associated with a building. A location of a user device within the building is identified. An overlay image is created that includes a three-dimensional depiction of a portion of a ductwork layout and a fire suppression system layout based on an overlay of the ductwork layout data and the fire suppression system layout data with respect to the building structural data and the location of the user device. The overlay image is output to a display component.

Abstract: The present invention relates to a cost effective and single step process for rapid manufacturing of paper-based SERS substrates (100), wherein chitosan is used for direct in situ reduction of the metallic precursor solutions for production of metallic nanoparticles on the substrates. The crucial step in the process involves the incubation of the paper-based substrates under humidifying conditions at an elevated temperature for a predetermined duration. The metal nanoparticles thus produced are homogenously deposited over the paper-based substrate making the paper-based substrate suitable for SERS analysis. The paper-based substrate thus developed is cost-effective, flexible, easy to load and is demonstrated to have exceptional sensitivity with detection limits of up to 1 pM.

Abstract: Techniques for searching documents are described. An exemplary method includes receiving a document search query; querying at least one index based upon the document search query to identify matching data; fetching the identified matched data; determining one or more of a top ranked passage and top ranked documents from the set of documents based upon one or more invocations of one or more machine learning models based at least on the fetched identified matched data and the document search query; and returning one or more of the top ranked passage and the proper subset of documents.

Abstract: Computer-implemented techniques are described herein for generating and utilizing a universal encoder component (UEC). The UEC maps a linguistic expression in a natural language to a language-agnostic representation of the linguistic expression. The representation is said to be agnostic with respect to language because it captures semantic content that is largely independent of the syntactic rules associated with the natural language used to compose the linguistic expression. The representations is also agnostic with respect to task because a downstream training system can leverage it to produce different kinds to machine-trained components that serve different respective tasks. The UEC facilitates the generation of downstream machine-trained models by permitting a developer to train a model based on input examples expressed in a language j?, and thereafter apply it to the interpretation of documents in language j?, with no additional training required.

Abstract: This document relates to natural language processing using a framework such as a neural network. One example method involves obtaining a first document and a second document and propagating attention from the first document to the second document. The example method also involves producing contextualized semantic representations of individual words in the second document based at least on the propagating. The contextualized semantic representations can provide a basis for performing one or more natural language processing operations.

Abstract: Aspects of the invention are directed towards a system and a method for configuring a communication interval of one or more sensing devices. One or more embodiments of the invention describe a method comprising steps of receiving a signal strength value from one or more sensing devices and determining an apparent distance between a regulating device and each of the one or more sensing devices based on the signal strength value. The method further comprising step dynamically ranking the one or more sensing devices based on the apparent distance and based on the ranking of the one or more sensing devices, configuring a communication interval for each of the one or more sensing devices.

Abstract: An air handling unit (AHU) system is provided and includes an AHU. The AHU includes an enclosure formed to define an inlet and at least one outlet downstream from the inlet, an air supply system to drive an air flow through the enclosure from the inlet to the outlet and a misting system operably coupled to the AHU. The misting system is configured to spray high pressure, cold water mist that includes mist droplets into a portion of the enclosure upstream from the at least one outlet whereupon heat transfer occurs directly between the mist droplets and air prior to the air reaching the outlet.

Abstract: The present invention relates to a stable pharmaceutical composition comprising vasopressin or pharmaceutically acceptable salts thereof. The present invention further provides a method of increasing blood pressure in adults with vasodilatory shock by administering said pharmaceutical composition of vasopressin or pharmaceutically acceptable salts thereof.

Abstract: A method and system are provided for handling a blockchain network based file storage system including a plurality of electronic devices.

Abstract: Transit voice assistant is a conversational voice-based assistant, accessible 24 hours a day and 7 days a week. It responds to user's request for real-time transit system information as well as transit alerts. Just say where you want to go to and transit voice assistant will make it happen. Transit voice assistant provides a unique experience for the customer by enabling the user to interact in a more intuitive way using only their voice. Transit voice assistant responds to the way users speak and think, without requiring users to type on a keyboard or screen. Transit voice assistant brings customers new levels of ease and convenience through voice technology, including natural language understanding and automatic speech recognition. The transit voice assistant is constantly learning and improves as more data is collected. Reach and delight more customers, where they are, through millions of voice powered devices.