Abstract: Methods and systems are provided for improving user authentication and access control by a network file system service in a multi-tenant public cloud environment by receiving a request for a connection to a file system from a file system client (client), sending an identification request for identification authentication of the client to a control system, receiving a response from the control system, establishing the connection to the file system upon determining that the connection to the file system is allowed based on cloud tenant information associated with the client, receiving an attempt to access the file system from the client by a sub-user, authenticating the sub-user based on the cloud tenant information, issuing a security token including a globally unique sub-user identifier of the sub-user, and using the security token to determine access rights of the sub-user to the file system for a subsequent request.

Abstract: A method may include executing a baseline classifier on unreviewed transaction features of an unreviewed transaction record to obtain a baseline account identifier, and executing a comparison model on (i) an unreviewed transaction vector of the unreviewed transaction record and (ii) reviewed transaction vectors to obtain comparison scores. The reviewed transaction vectors may correspond to reviewed transaction records each having a user-approved account identifier. The method may further include selecting, using the comparison scores, a reviewed transaction record. The reviewed transaction record may correspond to a comparison score. The comparison score may correspond to a user-approved account identifier of the reviewed transaction record.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a machine-learning model to determine predicted multi-level client intent classifications and provide a graphical user interface including selectable options for the predicted multi-level client intent classifications. In particular, in one or more embodiments, the disclosed systems utilize the machine-learning model to generate predicted multi-level client intent classifications and corresponding multi-level client intent classification probabilities. The disclosed systems can provide the multi-level client intent classifications to an agent device via a graphical user interface. Moreover, the disclosed systems can make recommendations and/or take action based on the predicted multi-level client intent classifications and corresponding multi-level client intent classification probabilities.

Abstract: Certain aspects of the present disclosure provide techniques for generating a user interface to prompt users of a software application to perform an action in the software application. The method generally includes generating historical transaction time gap data for transactions in the account. A probability distribution is generated based on the historical time gap data. The probability distribution represents a probability that a transaction related to the account has been performed after an elapsed time from a previous transaction. A probability that an unrecorded transaction exists for an account based on the probability distribution and a time difference between a most recent transaction and a current time. The probability that an unrecorded transaction exists is determined to exceed a threshold probability, and a user interface is generated and displayed to a user of the software application including a prompt for the user to enter new transactions for the account.

Abstract: The present disclosure provides methods and compositions for surface functionalization of solid substrates. The compositions include functionalized silanes and nucleic acid constructs which may react to immobilize the nucleic acid constructs on the surface on the solid substrate. The disclosure also provides methods for immobilization of silanes and nucleic acid constructs on the surface of the substrate.

Abstract: The present disclosure provides a composite machine learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine learning model is updated based on the descriptive string and the label. The machine learning model is then trained against the updated set of training data.

Abstract: Certain aspects of the present disclosure provide techniques for vectorization of transactions including: receiving electronic transaction information of one or more transactions of a user; for each transaction of the one or more transactions: segmenting the electronic transaction information of the transaction into one or more transaction words; generating a second transaction description related to the transaction; and identifying a category of the transaction; generating, based on the corresponding identified categories of the one or more transactions, a set of transaction history data of the user; providing the set of transaction history data of the user as an input to a machine learned model trained to output a set of word embedding vectors; determining, based on an output of the machine learned model comprising a set of word embedding vectors, a set of similar merchants; and providing the set of similar merchants for display to the user.

Abstract: Disclosed are a tissue processor and a method of processing a tissue sample by a tissue processor. The tissue processor includes a retort adapted to contain a material to be processed and having an opening, a retort lid configured to open and close the opening of the retort, and a heater coupled to the retort lid so as to heat the retort lid.

Abstract: A multi-mode switchable car includes a car body, a bogie, a wheel assembly, a steering driving module, a steering locking structure and a locking sensing structure. The car body is provided with a control module, and multiple wheel driving programs are preset. The bogie is rotatably connected to the car body. The wheel assembly includes a hub motor connected to the bogie or the car body and a wheel-type component detachably connected to the hub motor, wheel forms are switchable by connecting different wheel-type components to the hub motor. The steering driving module drives the bogie. The steering locking structure locks the bogie when located in a locking position. The locking sensing structure acquires a position of the steering locking structure and is electrically connected with the control module, and the control module switches the wheel driving programs according to position information acquired by the locking sensing structure.

Abstract: The present disclosure provides methods and compositions for surface functionalization of solid substrates. The compositions include functionalized silanes and nucleic acid constructs which may react to immobilize the nucleic acid constructs on the surface on the solid substrate. The disclosure also provides methods for immobilization of silanes and nucleic acid constructs on the surface of the substrate.

Abstract: A method that predicts business income from user transaction data. A multinomial classifier is trained, using a vector of features from data related to a historical transaction and a label associated with the historical transaction, to generate a probability that the historical transaction belongs to a specific classification with respect to income. Data related to a new transaction is split into a set of unigrams. A new vector of features is generated from the data related to the new transaction. The new vector includes a set of values that correspond and are assigned to the set of unigrams. A classification with respect to income is determined for the new transaction by applying the multinomial classifier to the new vector. The new transaction is labeled with the classification. One or more fields of a form that is maintained by an online service is populated using the classification.

Abstract: A method categorizes transaction records. A transaction record is received by a server application. The transaction record is encoded with a first machine learning model to obtain a transaction vector, wherein the transaction vector is in a same vector space as multiple account vectors. A second machine learning model executing in the server application, selects an account vector, from the multiple account vectors, corresponding to the transaction vector. An account identifier, corresponding to the account vector, is presented for the transaction record.

Abstract: A method utilizes a framework for transaction categorization personalization. A transaction record is received. a baseline model is selected from a plurality of machine learning models. An account identifier, corresponding to the transaction record using the baseline model, is selected. The account identifier for the transaction record is presented.

Abstract: The present disclosure provides methods, devices and systems that enable simultaneous multiplexing amplification reaction and real-time detection in a single reaction chamber.

Abstract: A method performs personalized transaction categorization. A transaction record is received, by a server application. In a first stage, sparse raw features are extracted from a transaction record of a transaction and converted into a transaction vector including dense features. In a second stage, the transaction vector is classified into a customized chart of accounts using the dense features to generate adapter model output. The method further includes selecting, an account identifier, corresponding to the transaction record and to an account of the customized chart of accounts, using the adapter model output, and presenting the account identifier for the transaction record.

Abstract: A method may include executing a baseline classifier on unreviewed transaction features of an unreviewed transaction record to obtain a baseline account identifier, and executing a comparison model on (i) an unreviewed transaction vector of the unreviewed transaction record and (ii) reviewed transaction vectors to obtain comparison scores. The reviewed transaction vectors may correspond to reviewed transaction records each having a user-approved account identifier. The method may further include selecting, using the comparison scores, a reviewed transaction record. The reviewed transaction record may correspond to a comparison score. The comparison score may correspond to a user-approved account identifier of the reviewed transaction record.

Abstract: Systems and methods that may be used to automatically correct, complete and or suggest words or terms to the user of an electronic service (e.g., accounting service) while the user is entering a search keyword and or filling out a form field. The automatic correction, completion and or suggestion of words or terms are based natural language processing of historical data from a plurality of users of the electronic service.

Abstract: Systems and methods that may be used to provide guidance and or tag suggestions to a user of an electronic accounting system and or service that overcome the shortcomings associated with user-defined tags.

Abstract: Systems and methods for generating recommended offers are disclosed. An example method may be performed by one or more processors of a recommendation system and include correlating attributes of users with attributes of offers based on historical data associated with the users and offers, training a machine learning model to predict a user's interest in an offer based on the correlating, obtaining current user data, obtaining current offer data, providing the current user data and the current offer data to the trained machine learning model, generating, using the trained machine learning model, a predicted level of interest that the current user has in each respective current offer of the number of current offers, identifying, among the number of current offers, at least one current offer having a predicted level of interest for the current user greater than a value, and generating one or more recommended offers for the current user.

Abstract: User data from users/consumers is transformed into machine learning training data including historical offer attribute model training data, historical offer performance model training data, and user attribute model training data associated with two or more users/consumers, and, in some cases, millions, tens of millions, or hundreds of millions or more, users/consumers. The machine learning training data is then used to train one or more offer/attribute matching models in an offline training environment. A given current user's data and current offer data are then provided as input data to the offer/attribute matching models in an online runtime/execution environment to identify current offers predicted to have a threshold level of user interest. Recommendation data representing these offers is then provided to the user and the current user's actions with respect to the recommended offers is monitored and used as online training data.