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: Disclosed are a computer-implemented method, a system and a computer program product for feature processing. In the computer-implemented method for feature processing, two input features selected from multiple features of each sample in a sample set are projected to one resulting feature by one or more processing units based on a specified curve. The sample set is updated by replacing the two input features with the one resulting feature for each sample in the sample set by one or more processing units. The projecting and the updating for the sample set are repeated by one or more processing units until the number of features of each sample in the sample set reaches a predetermined criterion.

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: Disclosed are a caffeic acid-based composite material and a preparation method thereof. The method includes: exposing a cyclodextrin metal-organic framework material prepared from ?-cyclodextrin to a solution of caffeic acid in a short-chain alcohol to obtain a mixed material; and incubating the mixed material, wherein during the incubating, the cyclodextrin metal-organic framework material is in dynamic contact with the solution of caffeic acid in a short-chain alcohol. The prepared composite material includes a cyclodextrin metal-organic framework material and caffeic acid loaded on the cyclodextrin metal-organic framework material, wherein the cyclodextrin metal-organic framework material is prepared from ?-cyclodextrin. The caffeic acid is loaded in an amount of 15-18% of a total mass of the caffeic acid-based composite material. The caffeic acid is located in a cavity of the cyclodextrin metal-organic framework material.

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).

Abstract: Hybrid use of dual policies is provided to improve a communication system. In a multiple access scenario, when an inactive user equipment (UE) transitions to an active state, it may be become a burden to a radio cell on which it was previously camping. In some embodiments, hybrid load balancing is provided using a hierarchical machine learning paradigm based on reinforcement learning in which an LSTM generates a goal for one policy influencing cell reselection so that another policy influencing handover over active UEs can be assisted. The communication system as influenced by the policies is modeled as a Markov decision process (MDP). The policies controlling the active UEs and inactive UEs are coupled, and measureable system characteristics are improved. In some embodiments, policy actions depend at least in part on energy saving.

Abstract: A method for extracting and isolating a lutein crystal from a vegetable oil resin containing a lutein diester, comprises: dissolving lipase into deionized water to form an enzyme solution; dissolving a lutein extract into an alcohol solvent containing the deionized water to form a uniform alcohol solution; adding the enzyme solution to the alcohol solution for performing hydrolysis, and stirring same to obtain a lutein solution; filtering and performing filtration isolation on the lutein solution to obtain a crystalline; re-dissolving the crystalline into a non-polar organic solvent, and using deionized water for washing a water-soluble impurity; recycling and cooling the organic solvent to obtain a recrystallization; and isolating and drying the recrystallization to obtain the lutein crystal. In this method, selectivity is strong, reaction time is short, no waste water is produced, process is environment-friendly and suitable for industrial production, and obtained lutein crystal is high in purity and yield.

Abstract: A computer-implemented method for compressing a machine learning model includes converting an input machine learning model into a standard machine learning model. The method further includes converting the standard machine learning model into a plurality of pruned machine learning models, each of the pruned machine learning models converted using a corresponding pruning ratio from a pruning ratio candidate list. The method further includes determining, for each of the pruned machine learning models, a size-to-error ratio. The method further includes selecting, based on the size-to-error ratio of the pruned machine learning models, a first pruning ratio from the pruning ratio candidate list. The method further includes generating a compressed machine learning model by compressing the input machine learning model using the first pruning ratio that is selected. The method further includes deploying the compressed machine learning model for production.

Abstract: A method, computer program, and computer system are provided for load forecasting. Datasets corresponding to source machine learning models and a target domain base model are identified. A set of forecasting models corresponding to the identified datasets are learned. An ensemble model is determined from the learned set of forecasting models based on gradient boosting. An available resource is allocated based on the ensemble model.

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: Methods, systems, and apparatuses for managing sensor data, including receiving encoded data at a first device from a second device separate from the first device, wherein the encoded data is generated using an artificial intelligence (AI) encoder model included in the second device based on sensor data collected by at least one sensor included in the second device; providing the encoded data to an AI inference model to obtain inference information; and performing a task based on the inference information, wherein the AI encoder model and the AI inference model are jointly trained based on an output of an AI teacher model

Abstract: An intelligent sterilization, deodorization and carbon-releasing reduction garbage bin includes a bin body, a bin cover disposed on the bin body, a solution storage chamber and a spray head. The spray head is disposed on a wall of the bin body or on the bin cover. By disposing an inner bucket and/or a garbage bag each with a liquid storage cavity, a molding groove, the liquid storage chamber, the spray head and an intelligent control device, the solution storage chamber being for storing a sterilization and deodorization solution, and the spray head being for spraying the solution stored in the liquid storage chamber, which can filter out water in garbage uninterruptedly and spray a special liquid with effects of sterilization, deodorization and carbon-releasing reduction into the garbage as needed and in an automatic timed and quantitative manner, thereby comprehensively playing multiple beneficial effects in sterilization, deodorization and carbon-releasing reduction.

Abstract: An apparatus distributing communication load over a plurality of communication cells may select action centers from random cell reselection values, based on a standard deviation of an internet protocol (IP) throughout over the plurality of communication cells; input a first vector indicating a communication state of a communication system and a second vector indicating the standard deviation of the IP throughout of the plurality of communication cells, to a neural network to output a sum of the action centers and offsets as cell reselection parameters; and transmit the cell reselection parameters to the communication system to enable a base station of the communication system to perform a cell reselection based on the cell reselection parameters.

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: 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: Embodiments of the present disclosure relate to weight matrix prediction. In an embodiment, a computer-implemented method is disclosed. The method comprises sending a candidate weight matrix of a neural network to one of a plurality of computing nodes comprised in a computing system to perform a testing iteration. The method further comprises receiving a testing loss value from the one of the plurality of computing nodes based on the testing iteration. The method further comprises evaluating whether the testing loss value is applicable. The method further comprises determining that the candidate weight matrix is available to be employed in a new formal iteration in response to the testing loss value being applicable. In other embodiments, a system and a computer program product are disclosed.

Abstract: Hybrid use of dual policies is provided to improve a communication system. In a multiple access scenario, when an inactive user equipment (UE) transitions to an active state, it may be become a burden to a radio cell on which it was previously camping. In some embodiments, hybrid load balancing is provided using a hierarchical machine learning paradigm based on reinforcement learning in which an LSTM generates a goal for one policy influencing cell reselection so that another policy influencing handover over active UEs can be assisted. The communication system as influenced by the policies is modeled as a Markov decision process (MDP). The policies controlling the active UEs and inactive UEs are coupled, and measureable system characteristics are improved. In some embodiments, policy actions depend at least in part on energy saving.

Abstract: Hybrid use of dual policies is provided to improve a communication system. In a multiple access scenario, when an inactive user equipment (UE) transitions to an active state, it may be become a burden to a radio cell on which it was previously camping. In some embodiments, hybrid load balancing is provided using a hierarchical machine learning paradigm based on reinforcement learning in which an LSTM generates a goal for one policy influencing cell reselection so that another policy influencing handover over active UEs can be assisted. The communication system as influenced by the policies is modeled as a Markov decision process (MDP). The policies controlling the active UEs and inactive UEs are coupled, and measureable system characteristics are improved. In some embodiments, policy actions depend at least in part on energy saving.

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.