Abstract: The present disclosure provides for methods, apparatuses, and non-transitory computer-readable storage media for load balancing traffic scenarios by a network device. In an embodiment, a method includes training a plurality of learning agents to load balance a respective plurality of traffic scenarios to obtain a plurality of control policies. The method further includes performing at least one clustering iteration. Each clustering iteration includes selecting a pair of control policies and merging the pair of control policies into a clustered control policy that replaces the pair of control policies. The method further includes determining to stop the performing of the at least one clustering iteration when a quantity of control policies remaining in the plurality of control policies meets a predetermined value. The method further includes deploying to each base station of a plurality of base stations a corresponding control policy from the plurality of control policies.

Abstract: Use of a compound of Formula I for treating atherosclerosis and related diseases, disorders and conditions in a subject in need of such treatment, wherein the subject in need of such treatment is a subject carrying one or more genetic mutations in ALPK1.

Abstract: A method of forecasting a future load may include: obtaining source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively; among a plurality of source machine learning models, selecting at least one machine learn source model that has a traffic load prediction performance higher than that of a target machine learning model through a negative transfer analysis; obtaining model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models; obtaining a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights; and predicting a future communication traffic load of the target base station based on the load

Abstract: The present invention discloses a method for treating ROSAH syndrome, specifically relates to a method for inhibiting ALPK1 or ALPK1[T237M]. The ALPK1 inhibitor has excellent pharmacokinetic properties, and has the potential to serve as a precision-targeted drug for the treatment of ROSAH syndrome.

Abstract: A series of peptide-drug conjugates that react with multi-arm polyethylene glycol (PEG) to form chemically cross-linked hydrogels and their use as both a sealant and a therapeutic depot is disclosed.

Abstract: Embodiments of the present disclosure provide systems and methods for implementing self-bias detection based on performance and importance. A disclosed computer implemented method aggregates continuous input data through a K-means clustering algorithm to reduce the number of aggregated sub-group data pairs, enabling a reduced calculation time for computing bias and enhanced performance. The self-bias detection identifies a scale factor and a balance factor of aggregated sub-group data pairs, which indicate the importance of the detected bias.

Abstract: Embodiments of the present disclosure provide systems and methods for implementing self-bias detection based on performance and importance. A disclosed computer implemented method aggregates continuous input data through a K-means clustering algorithm to reduce the number of aggregated sub-group data pairs, enabling a reduced calculation time for computing bias and enhanced performance. The self-bias detection identifies a scale factor and a balance factor of aggregated sub-group data pairs, which indicate the importance of the detected bias.

Abstract: Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.

Abstract: A method performed by at least one processor of a network device in communication with a plurality of base stations, the method including: receiving historical data collected by one or more base stations from the plurality of base stations, the historical data indicating one or more of a power consumption, handover data, and quality of service (QOS); generating, from the historical data, training data comprising a plurality of cell states and a corresponding random action for each cell state; and training one or more neural network estimators based on the training data, where the one or more neural network estimators comprise one or more of a power consumption estimator, a QoS estimator, and a handover prediction estimator, and where each base station from the plurality of base stations is associated with a respective cell.

Abstract: Rapid and data-efficient training of an artificial intelligence (AI) algorithm are disclosed. Ground truth data are not available and a policy must be learned based on limited interactions with a system. A policy bank is used to explore different policies on a target system with shallow probing. A target policy is chosen by comparing a good policy from the shallow probing with a base target policy which has evolved over other learning experiences. The target policy then interacts with the target system and a replay buffer is built up. The base target policy is then updated using gradients found with respect to the transition experience stored in the replay buffer. The base target policy is quickly learned and is robust for application to new, unseen, systems.

Abstract: The present disclosure provides methods, apparatuses, systems, and computer-readable mediums for operating a target base station by an apparatus. A method includes collecting a plurality of trajectories corresponding to the target base station and a plurality of source base stations, clustering, using an unsupervised reinforcement learning model, the plurality of trajectories into a plurality of clusters including a target cluster, selecting, as a target trajectory, a selected trajectory from the target cluster that maximizes an energy-saving parameter of the target base station, and applying, to the target base station, an energy-saving control policy corresponding to the target trajectory. The target cluster corresponds to the target base station and at least one source base station from among the plurality of source base stations.

Abstract: A computer-implemented method, a system and a computer program product for device failure detection are disclosed. In the method, phase-based predictions may be performed on a plurality of storage devices to determine a plurality of sampling scopes and corresponding sampling ratios. The respective sampling scopes may comprise at least one storage device of the plurality of storage devices. A sampling dataset may be obtained by selecting a group of storage devices from the respective sampling scopes with the corresponding sampling ratios. Device failure may be detected for the group of storage devices based on the sampling dataset.

Abstract: Transfer learning based on prediction determines a similarity between a source base station and a target base station. Importance of parameters is determined and training is adjusted to respect the importance of parameters. A lack of historical data is compensated by selecting a base station as source base station which has a larger amount of historical data.

Abstract: Provided herein are methods for inhibiting ALPK1 kinase activity using a compound of Formula I, and compositions and methods for therapy, for example in treating Kawasaki Disease.

Abstract: Provided herein are compounds of Formula (I) or (II) and related compositions and methods for their use as inhibitors of alpha-kinase 1 (ALPK1) and in treating Kawasaki Disease.

Abstract: A method is provided. The method includes receiving a first dimension set, extracting a first latent feature set from the first dimension set, training a first base predictor based on the first feature set, generating a second dimension set based on the first dimension set, the second dimension set having fewer dimensions than the first dimension set, extracting a second latent feature set from the second dimension set, training a second base predictor based on the second feature set, and generating a traffic prediction based on the first base predictor and the second base predictor.

Abstract: Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.

Abstract: The disclosure provides compositions and methods related to activating alpha-kinase 1 (ALPK1) for modulating an immune response and treating or preventing cancer, infection, inflammation and related diseases and disorders as well as potentiating an immune response to a target antigen. The disclosure also provides heterocyclic compounds of formula (I) as agonists of alpha protein kinase 1 (ALPK1) and their use in activating ALPK1, modulating an immune response and treating diseases such as cancer, wherein A1, A2, L1, L2, L3, Z1, Z2, W1, W2, R1, R2, R3, R4, R5, R6 and R7 are defined herein.

Abstract: Provided are compounds of Formula (I) or (II) and related compositions and methods for their use as inhibitors of alpha-kinase 1 (ALPK1).

Abstract: Provided are compounds of Formula I, compositions and methods for their use as inhibitors of alpha-kinase 1 (ALPK1).