Abstract: Methods and systems are disclosed for generating recommendations for causes of computer alerts that are automatically detected by a machine learning algorithm are described. For example, the system may receive a first feature vector with an unknown alert status, wherein the first feature vector represents values corresponding to a plurality of computer states in a first computer system. The system may input the first feature vector into an artificial neural network, wherein the artificial neural network is trained to detect a known alert status based on a set of training data comprising labeled feature vectors corresponding to the known alert status, and wherein the artificial neural network is trained to detect conditional expectations the plurality of computer states in an inputted feature vector.

Abstract: Disclosed are methods of isolating T cells having antigenic specificity for a mutated amino acid sequence encoded by a cancer-specific mutation, the method comprising: identifying one or more genes in the nucleic acid of a cancer cell of a patient, each gene containing a cancer-specific mutation that encodes a mutated amino acid sequence; inducing autologous APCs of the patient to present the mutated amino acid sequence; co-culturing autologous T cells of the patient with the autologous APCs that present the mutated amino acid sequence; and selecting the autologous T cells. Also disclosed are related methods of preparing a population of cells, populations of cells, pharmaceutical compositions, and methods of treating or preventing cancer.

Abstract: In some implementations, a remediation engine may receive an indication of the cloud-based application. The remediation engine may generate the data structure for the cloud-based application. The remediation engine may receive an indication of a corresponding user. The remediation engine may update the data structure to associate the corresponding user with the cloud-based application. The remediation engine may receive an indication of a corresponding category. The remediation engine may update the data structure to associate the corresponding category with the cloud-based application. The remediation engine may receive an indication of a first label and an indication of a first module of the cloud-based application. The remediation engine may update the data structure to associate the first label with the first module of the cloud-based application. The remediation engine may provide the data structure for processing alerts associated with the cloud-based application.

Abstract: The present invention relates to a novel Brassica juncea varieties designated PWRG-1, PWRG-2, and PWSGC, which have a reduced pungent flavor and/or odor traits. The invention provides seeds of the varieties PWRG-1, PWRG-2, and PWSGC, plants and parts thereof of the varieties, a tissue culture derived from the varieties, hybrids produced from varieties, and lines derived from varieties, as well as genetically modified forms of the foregoing plants and tissue culture. Also provided are methods of producing PWRG-1, PWRG-2, and PWSGC plants, hybrid plants, and lines derived from varieties PWRG-1, PWRG-2, and PWSGC. In addition, products produced from the plants of the present invention are provided.

Abstract: Disclosed herein are systems and methods of executing scanning software, such an executable software program or script (e.g., PowerShell script), by a computing device of an enterprise, such as a security server, may instruct the computing device to search all or a subset of computing devices in an enterprise network. The scanning software may identify PowerShell scripts containing particular malware attributes, according to a malicious-code dataset. The computing system executing the scanning software may scan through the identified PowerShell scripts to identify particular strings, values, or code-portions, and take a remedial action according to the scanning software programming.

Abstract: A system includes one or more processors and memory storing processor-executable instructions that cause the one or more processors to perform operations. The operations include generating a driving strategy for a traveling route of a train comprising at least one battery-electric locomotive (BEL) based on saved data in the system for optimizing one or more aspects for the train over the traveling route; operating the train according to the driving strategy; receiving update data associated with a current location of the train; revising the driving strategy based on the saved data and the update data for optimizing energy consumption efficiency for a segment of the traveling route by identifying active powered components of the train that are non-essential for traversing the segment and turning off power to one or more of the identified active powered components for a duration of traversing the segment.

Abstract: A method includes collecting, by a first AI application of a first AI computer, data to include in a dataset. The first AI application of the first AI computer can include an offer for the dataset into an offering platform. The first AI application of the first AI computer can then receive a response to the offer from a second AI application of a second AI computer. The first AI application of the first AI computer can determine whether or not to provide the dataset to the second AI application of the second AI computer. The first AI application of the first AI computer can, based on the determination, provide the dataset to the second AI application of the second AI computer.

Abstract: A method and related system of operations include obtaining, from a client computing device, a request comprising a request header field that is populated with a first domain name and updating the request by modifying the request header field to comprise a second domain name. The method further includes sending the updated request to a server, receiving a response comprising a response header, and updating the response by modifying a response header field of the response header to indicate the first domain name as an origin domain of the response. The method further includes sending the updated response to the client computing device.

Abstract: A workplace safety system includes an item of monitored equipment, a mobile communications device, and a scoring computing system. The item of monitored equipment includes a sensor, a transmitter, and a computing unit. The sensor is configured to generate signals. The computing unit is electrically coupled with each of the sensor and the transmitter. The mobile communications device is in wireless communication with the transmitter via a short-range communication protocol. The scoring computing system is in networked communication with the mobile communications device and is configured to receive data. The data includes an identity of an operator of the item of monitored equipment. A characteristic of operation is determined based upon the signals from the sensor and represents a manner by which the operator has used the item of monitored equipment. The scoring computing system is configured to generate a safety score associated with the operator. Methods are also provided.

Abstract: Systems as described herein may include authorizing the sharing of data and sharing data between a variety of systems. A request to share data may be provided to a first system. The system may create sharing session data using smart contracts executed by a distributed network system. Sharing session data may be stored using a smart contract. A second system may obtain the sharing session data and verify the sharing session based on the execution of the smart contract. On verification of the sharing session, a variety of data may be shared between the systems identified in the sharing session data. The sharing session data may be established between two systems and/or a number of systems. Smart contracts may provide a variety of functions for authorizing the sharing of data between systems. Additionally, encrypted data may be stored and/or obtained using a smart contract.

Abstract: In some implementations, a multi-purpose device may include a secure charging location for charging mobile devices. The multi-purpose device may receive authentication information associated with a user. The multi-purpose device may communicate with a fraud detection device, to authenticate the user and to identify that the user is associated with an account. The multi-purpose device may detect that a mobile device associated with the user has been placed in the secure charging location. The multi-purpose device may transmit to the fraud detection device and based on authenticating the user, an indication that the mobile device associated with the user has been placed in the secure charging location.

Abstract: Single-domain monoclonal antibodies (“nanobodies”) that specifically bind the S2 subunit of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein are described. The single-domain antibodies were isolated from shark variable domain of new antigen receptor (VNAR) and camel variant domain of heavy chain only antibody (VHH) phage display libraries panned against the S2 subunit of SARS-CoV-2 spike protein. The S2 subunit-specific nanobodies, and conjugates thereof, can be used for the diagnosis and treatment of a coronavirus infection.

Abstract: The disclosure addresses problems associated with a systematic integration of multi-modal data for effective training, and handling of large volume of data because of high resolution of the multiple modalities. Embodiments herein provide a method and a system for a distributed training of a multi-modal data fusion transformer. Herein, a distributed training approach called a Distributed Architecture for Fusion-Transformer Training Acceleration (DAFTA) is proposed for processing large multimodal remote sensing data. DAFTA is enabled to handle any combination of remote sensing modalities. Additionally, similarity of feature space is leveraged to optimize the training process and to achieve the training with reduced data set which is equivalent to a complete data set. The proposed approach provides a systematic and efficient method for managing large sensing data and enables accurate and timely insights for various applications.

Abstract: At least some embodiments are directed to an entity classification system that receives informational data associated with an entity. The informational data includes sentences associated with the entity. The entity classification system utilizes a first machine learning model to determine a first contextual meaning among words of a sentence associated with the entity based on a first word embedding technique, and determines at least one category associated with the entity based at least in part on the first contextual meaning. The entity classification system utilizes a second machine learning model to determine a second contextual meaning shared by a set of sentences based on a second embedding technique, and determines a subcategory of the category associated with the entity based at least in part on the second contextual meaning. The entity classification system generates an output including the category and subcategory associated with the entity.

Abstract: A graphical user interface and a system are provided which include a route identification interface element which specifies a new route for a vehicle, a plurality of route type buttons, a vehicle type interface, a stop order interface, a map, a control button, and an assignment button. The stop order interface identifies one or more stops for a vehicle in a fleet of vehicles. The map identifies the new route and the one or more stops graphically on the graphical user interface. The control button provides a utilization view for the one or more vehicles in the fleet of vehicles from which a vehicle from the one or more vehicles in the fleet of vehicles that are available can be assigned to the new route.

Abstract: Unlike visual similarity, visual compatibility is a complex concept. Existing approaches for outfit compatibility prediction does not focus on methods with personalization. The present disclosure proposes a novel approach to model the user's preference for different styles. The outfit compatibility prediction module is a critical component of an outfit recommendation system. An outfit is said to be compatible if all the items are visually compatible and match the user's preferences. The present disclosure represents the outfit as a graph and uses Graph Neural Network (GNN) with attention mechanism to capture the inter-relationship between the items. A graph read-out layer generates the final outfit embedding. The proposed approach efficiently models the preferences of the users for different styles. Finally, the outfit compatibility score is generated by computing the similarity between the outfit embedding and the user embedding.

Abstract: A method and related system may analyze metadata associated with a first set of transactions to determine whether to perform a second set of transactions. The method and related system may include determining a data category based on first device-provided data of a first database transaction indicating a first record associated with a second record, and may further include obtaining, based on whether the data category satisfies a first set of criteria, an identifier and an amount based on image data using a prediction model. The method may further include querying a database based on the identifier to obtain an indication that the identifier is mapped to an object category, validating the amount based on a result indicating whether the object category satisfies a second set of criteria, and causing a second transaction that changes fields of the first and second records.

Abstract: Disclosed herein is a transaction instrument and method for recording vibration information associated with a transaction at a point-of-service (POS) terminal. The vibration information can be used to determine whether the POS terminal is compromised (e.g., via a skimmer).

Abstract: A device assembly includes a controller, which is configured to control the nicotine electronic vaping device to output an indication of a current level of the nicotine pre-vapor formulation in the nicotine reservoir of a nicotine pod assembly in response to determining that an aggregate amount of nicotine pre-vapor formulation drawn from the nicotine reservoir or an aggregate amount of vaporized nicotine pre-vapor formulation is greater than or equal to the at least one nicotine pre-vapor formulation level threshold.

Abstract: A nicotine lozenge includes a body that is partially or wholly receivable in an oral cavity. The body includes a soluble-fiber matrix and nicotine or a derivative thereof dispersed in the soluble-fiber matrix. A nicotine lozenge may include at least 40 weight percent of soluble fiber. Soluble fiber in a nicotine lozenge may include maltodextrin. The nicotine lozenge is adapted to release the nicotine or a derivative thereof from the body when the body is received within the oral cavity of an adult consumer and exposed to saliva. A method of making nicotine lozenges includes forming a molten mixture of at least 40 weight percent soluble fiber, nicotine, and less than 15 weight percent water while maintaining a mixture temperature of less than 150° C. and portioning the molten mixture into a plurality of nicotine lozenges. The ingredients can be mixed to form the molten mixture in an extruder.