Abstract: In some implementations, a web host may receive a configuration file associated with at least one action. The web host may determine, using the configuration file, that the at least one action is controlled for concurrency. Accordingly, the web host may generate a URL for the at least one action. The URL may include a slug, and the slug may include an identifier associated with the action and at least one bit indicating that the action is controlled for concurrency.

Abstract: Disclosed are systems and methods for identifying prostate cancer patients at high-risk of progression among clinically intermediate risk group. Images of patient cells are obtained and tiled into subsets of smaller images. Using a trained machine learning model, a morphology quantification process is performed on the subsets of smaller images. Portions of the images are input into the trained machine learning models. The trained machine learning model determines the occurrence of likely cancer cells and classifies the cancer cells with a grading. The system then uses this out of the machine learning model to identify whether the cells in the subsets of smaller images indicate whether a prostate cancer patient is at a risk of progression among a clinically intermediate risk group.

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: Some implementations described herein relate to a system for generating identifiers. The system receives, from a device, a request to generate a plurality of identifiers. The system determines, based on receiving the request and by performing a parameter locking operation, a compute region parameter associated and a timestamp parameter. The system generates, based on the compute region parameter and the timestamp parameter, a set of one or more identifiers of the plurality of identifiers. Respective portions of each identifier, of the set of one or more identifiers, may indicate the compute region parameter, a version parameter associated with the parameter locking operation, the timestamp parameter, a counter value parameter, and an action parameter. The system processes, using a scrambling technique, the set of one or more identifiers and provides the set of one or more identifiers to the device.

Abstract: In some implementations, a web instance may receive an indication of an event associated with a uniform resource locator (URL) that includes a slug. The web instance may determine, using an indicator encoded in the slug, that the URL is associated with an action that is included in a concurrency control group. The web instance may communicate with a remote database to determine whether the concurrency control group is locked. The web instance may selectively trigger execution of the action based on whether the concurrency control group is locked.

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: Techniques and systems are provided for interfacing one or more event detectors coupled by a common interconnect with a network interface. Interconnect signals indicative of events detected by the one or more event detectors using sensor data are propagated on the common interconnect. A monitor device indirectly or directly couple to the common interconnect monitors the interconnect signals. Information about a first event detector of the one or more event detectors is inferred, in part, from the interconnect signals and transmitted via the network interface.

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: 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 identifying and quantifying molecular biomarkers and for predicting patient response to cancer therapy are provided. The disclosed methods and systems make use of artificial intelligence to capture morphometric changes from histopathology tissue that correlate with molecular changes. The system may analyze molecular markers which are predictive of tumor response and have no defined correlation with morphological features. The system may use artificial intelligence to correlate morphometric changes with critical gene modifications and molecular changes.

Abstract: Disclosed are systems and methods for predicting response of triple-negative breast cancer to neoadjuvant chemotherapy using a deep convolutional neural network-based artificial intelligence tool a method of predicting patient response to therapy. The system divides patient tissue image slides into multiple tiles. A convolutional neural network (CNN) is trained based on the multiple tiles. The system may perform artifact detection and cancer classification to identify patterns and features that capture tumor cell heterogeneity along with stromal and tumor micro environment (TME) components of a second set of patient tissue image tiles.

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.