Abstract: A transaction model of a general model generates a target transaction vector for a target transaction record. The general model also generates account vectors for accounts. A match score is generated between the account vectors and the transaction vector. The general model selects a first account identifier of an account using the match score. The transaction model also generates historical transaction vectors for historical transaction records. Further, a comparison score is generated between the historical transaction vectors and the target transaction vector. A second account identifier of an historical transaction is selected according to the comparison score. One of the first account identifier and the second account identifier is selected as the account identifier for the transaction record, and the transaction record is stored with the account identifier.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a theme classification from unstructured text. In particular, in one or more embodiments, the disclosed systems receive experience data comprising an experience score and unstructured text. The disclosed systems can utilize a theme-classifying machine-learning model to generate a theme classification from the unstructured text and associate the theme classification to the experience score. Moreover, in some embodiments, the disclosed systems can determine and take actions based on the theme classification.

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: 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: 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 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 disclosure relates to an activity recognition method of a large receptive field (LRF) large-kernel attention convolution network based on a large receptive field, the method includes the following steps: collecting an action signal, carrying out a preprocessing and a data partition on the action signal to obtain a data set; and training an LRF large-kernel attention convolution network model based on the data set, and introducing a trained LRF large-kernel attention convolution network model into a mobile wearable recognition device for human posture recognition.

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