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 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 negative pressure wound therapy apparatus can include a wound dressing, a fluid collection container, a vacuum pump comprising a pump motor, and tubing. Additionally, the apparatus can include a pressure sensor that measures a pressure in the tubing. One or more tubes can channel a fluid between the wound dressing, the fluid collection canister, and the pump. In addition, first and second control circuits can be provided for controlling the pump motor without using a processor. The first control circuit can generate a difference signal between a desired pressure input and a pressure sensor input, and can further generate a motor control signal responsive to the difference signal. Moreover, a second control circuit can provide an override signal based at least in part on the difference signal and at least one reference signal. The override signal beneficially overrides the motor control signal to prevent the pump motor from stalling.

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 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: 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: 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: 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: 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: 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: Etoposide glycosides and methods of making etoposide glycosides are disclosed. Glycosyl transferases catalyze addition of one or more monosaccharides to etoposide to yield etoposide glycosides. Suitable monosaccharides can be in the L- or D-configuration and typically have 5, 6, or 7 carbons. Suitable monosaccharides include allose, apiose, arabinose, fructose, fucitol, fucose, galactose, glucose, glucuronic acid, mannose, A-acetylglucosamine, rhamnose, or xylose. Uridine diphosphate glycosyl transferases can catalyze formation of either an alpha or beta glycosidic bond.

Abstract: Enasidenib glycosides and methods of making enasidenib glycosides are disclosed. Glycosyl transferases catalyze addition of one or more monosaccharides to enasidenib to yield enasidenib glycosides. Suitable monosaccharides can be in the L- or D-configuration and typically have 5, 6, or 7 carbons. Suitable monosaccharides include allose, apiose, arabinose, fructose, fucitol, fucose, galactose, galacturonate, glucose, glucuronic acid, mannose, N-acetylglucosamine, rhamnose, or xylose. Uridine diphosphate glycosyl transferases can catalyze formation of either an alpha or beta glycosidic bond.

Abstract: A method includes obtaining at least one policy parameter of a neural network corresponding to a load balancing policy, receiving trajectories for each mobile device in a plurality of mobile devices of the wireless network, each trajectory corresponding to a sequence of states of a respective mobile device, wherein the sequence of states is generated based on a continuous interaction of an existing policy of the respective mobile device with the wireless network, estimating advantage functions for each mobile device in the plurality of mobile devices based on the trajectories for each respective mobile device, and updating the at least one policy parameter based on the estimated advantage functions such that the load balancing policy is determined based on states of each mobile device in the plurality of mobile devices.

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: Ivacaftor glycosides and methods of making ivacaftor glycosides are disclosed. Glycosyl transferases catalyze addition of one or more monosaccharides to ivacaftor to yield ivacaftor glycosides. Suitable monosaccharides can be in the L- or D-configuration and typically have 5, 6, or 7 carbons. Suitable monosaccharides include allose, apiose, arabinose, fructose, fucitol, fucose, galactose, glucose, glucuronic acid, mannose, A-acetylglucosamine, rhamnose, or xylose. Uridine diphosphate glycosyl transferases can catalyze formation of either an alpha or beta glycosidic bond.

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.