Abstract: Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.

Abstract: Methods and systems disclosed herein collect user data in real-time and organize them (e.g., using collaborative filtering) into groups (e.g., clusters). The system then determines statistical distributions of observed real-time intents (e.g., based on actual selections made by users) for each group. The system then merges this distribution with existing model predictions (e.g., a model trained on historical training data) to balance between historical and dynamically updated information.

Abstract: Methods and systems for dynamically updating machine learning models that provide conversational responses through the use of a configuration file that defines modifications and changes to the machine learning model are disclosed. For example, the configuration file may be used to define an expected behavior and required attributes for instituting modifications and changes (e.g., via a mutation algorithm) to the machine learning model.

Abstract: In some aspects, methods and systems may use consumption timestamps to ensure consistency between data used during inference and data used to train a machine learning model. Specifically, methods and systems may generate a consumption timestamp corresponding to the time at which the streaming data is received or inserted into a database. The consumption timestamp may be used as a synchronization point for any related data that may be requested (e.g., via an API). After streaming data is received, the consumption timestamp may be generated and assigned to any data that is requested in connection with the streaming data. The streaming data, related data, and consumption timestamp may be included in a data snapshot that is provided to a machine learning model for inference (e.g., real-time inference) or training.

Abstract: Methods and systems for dynamically updating machine learning models that provide conversational responses through the use of a configuration file that defines modifications and changes to the machine learning model are disclosed. For example, the configuration file may be used to define an expected behavior and required attributes for instituting modifications and changes (e.g., via a mutation algorithm) to the machine learning model.

Abstract: Methods and systems disclosed herein collect user data in real-time and organize them (e.g., using collaborative filtering) into groups (e.g., clusters). The system then determines statistical distributions of observed real-time intents (e.g., based on actual selections made by users) for each group. The system then merges this distribution with existing model predictions (e.g., a model trained on historical training data) to balance between historical and dynamically updated information.

Abstract: This invention claims Individually Ventilated Caging system (IVC system) for small animals comprising a Ventilation Unit (1) comprising an initial supply chamber (12) for taking external air inside the system for filtering and supplying to the cages in IVC system and an exhaust chamber (22) for exhausting the used air collected from the cages in the IVC system; the supply chamber and the exhaust chamber provided with filter/s (13),(14), (17), (18) for the incoming or outgoing/exhausting air; wherein the IVC system is characterized by devices additional to the filters for disinfecting incoming air and/or disinfecting out-going air exhausted from the cages. The additional devices comprise (a) UVC light (15), and/or (b) one or more electrostatic air filter/s and/or (3) an ionizer on supply and or exhaust side.

Abstract: A process and system for monitoring and controlling the operation of a dehydrogenation reactor is provided. Samples of hydrocarbon streams are taken at sampling locations to be analyzed at a single gas chromatograph or other analytical equipment. Actions can be taken to modify the operation of the dehydrogenation reactor as necessary to maintain its operation within predetermined parameters. In particular, actions may be taken when a hydrocarbon stream exhibits an amount of cracking that is outside parameters. It is usually intended that actions will be taken on a gradual basis once or twice per day to reduce the cost of the process while still providing the necessary changes to operations.

Abstract: Methods, systems and computer program products for multi-style text transformation are provided herein. A computer-implemented method includes selecting at least one set of style specifications for transforming at least a portion of input text. The at least one set of style specifications include one or more target writing style domains selected from a plurality of writing style domains, weights for each of the target writing style domains representing relative impact of the target writing style domains for transformation of at least a portion of the input text, and weights for each of a set of linguistic aspects for transformation of at least a portion of the input text. The computer-implemented method also includes generating one or more style-transformed output texts based at least in part on the at least one set of style specifications utilizing at least one unsupervised neural network.

Abstract: A method for distributing and managing an Input/Output (IO) request in a disaggregated storage architecture includes receiving the IO request including IO data to be distributed in the disaggregated storage architecture, generating IO metadata corresponding to the IO data included in the received IO request, determining one or more controller parameters for each of a plurality of controllers of the disaggregated storage architecture, determining a first priority weight of each controller parameter of the one or more controller parameters based on a network type of the disaggregated storage architecture, determining a first IO management weight for each of the plurality of controllers based on the one or more controller parameters and corresponding first priority weights, and statically mapping each of the IO metadata and the IO data to at least one controller of the plurality of controllers based on the first IO management weights of the plurality of controller.

Abstract: A method for dynamically replicating data in a disaggregated distributed storage system includes receiving, by a processor, a request from a controller to replicate data; dynamically selecting, by the processor, a storage node for replicating the data from among a plurality of storage nodes, based on one or more replication parameters associated with each storage node of the plurality of storage nodes, wherein the one or more replication parameters include at least one of a flash factor corresponding to the each storage node, and a latency between the controller and the each storage node; and providing, by the processor, information about the selected storage node to the controller for replicating the data

Abstract: A method of performing replication recovery operation in a disaggregated distributed storage system (DDSS) includes identifying an affected storage node included in the storage pool based on an occurrence of one or more predetermined conditions; selecting at least one replacement storage node from among one or more storage nodes included in the storage pool to recover replication data stored in the affected storage node; selecting at least one controller for performing a replication recovery operation from among one or more controllers included in the controller pool; and assigning one or more replication recovery jobs to the at least one controller; and receiving, by the replication recovery device, a notification from the at least one controller based on the one or more replication recovery jobs being completed.

Abstract: Methods and systems for dynamically updating machine learning models that provide conversational responses through the use of a configuration file that defines modifications and changes to the machine learning model are disclosed. For example, the configuration file may be used to define an expected behavior and required attributes for instituting modifications and changes (e.g., via a mutation algorithm) to the machine learning model.

Abstract: Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.

Abstract: Methods and systems for dynamically updating machine learning models that provide conversational responses through the use of a configuration file that defines modifications and changes to the machine learning model are disclosed. For example, the configuration file may be used to define an expected behavior and required attributes for instituting modifications and changes (e.g., via a mutation algorithm) to the machine learning model.

Abstract: Methods, systems, and computer program products for unsupervised tunable stylized text transformations are provided herein. A computer-implemented method includes identifying amendable portions of input text by processing at least a portion of the input text using at least one neural network; determining stylistic text modifications to the amendable portions of the input text, the text modifications encompassing a set of stylistic parameters, wherein said determining comprises processing at least a portion of the set of stylistic parameters using at least one neural network; generating a stylized output set of text by transforming at least a portion of the input text, wherein said transforming comprises modifying at least one of the amendable portions of the input text via at least one of the stylistic text modifications encompassed by the set of stylistic parameters; and outputting the stylized output set of text to at least one user.

Abstract: Methods and systems disclosed herein collect user data in real-time and organize them (e.g., using collaborative filtering) into groups (e.g., clusters). The system then determines statistical distributions of observed real-time intents (e.g., based on actual selections made by users) for each group. The system then merges this distribution with existing model predictions (e.g., a model trained on historical training data) to balance between historical and dynamically updated information.

Abstract: A process and system for monitoring and controlling the operation of a dehydrogenation reactor is provided. Samples of hydrocarbon streams are taken at sampling locations to be analyzed at a single gas chromatograph or other analytical equipment. Actions can be taken to modify the operation of the dehydrogenation reactor as necessary to maintain its operation within predetermined parameters. In particular, actions may be taken when a hydrocarbon stream exhibits an amount of cracking that is outside parameters. It is usually intended that actions will be taken on a gradual basis once or twice per day to reduce the cost of the process while still providing the necessary changes to operations.

Abstract: Methods and systems disclosed herein collect user data in real-time and organize them (e.g., using collaborative filtering) into groups (e.g., clusters). The system then determines statistical distributions of observed real-time intents (e.g., based on actual selections made by users) for each group. The system then merges this distribution with existing model predictions (e.g., a model trained on historical training data) to balance between historical and dynamically updated information.

Abstract: Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.