Abstract: A method and apparatus for optimizing a recommendation system, a device and a computer storage medium are described, which relates to the technical field of deep learning and intelligent search in artificial intelligence. A specific implementation solution is: taking the recommendation system as an agent, a user as an environment, each recommended content of the recommendation system as an action of the agent, and a long-term behavioral revenue of the user as a reward of the environment; and optimizing to-be-optimized parameters in the recommendation system by reinforcement learning to maximize the reward of the environment. The present disclosure can effectively optimize long-term behavioral revenues of users.

Abstract: A method and apparatus for building a route time consumption estimation model, and a method and apparatus for estimating a route time consumption are provided.

Abstract: A computer-implemented method is provided. The method includes: obtaining feature information of a molecule to be represented, wherein the molecule includes a plurality of atoms; generating a fully connected graph of the plurality of atoms, wherein the fully connected graph includes a plurality of edges; generating, based on the feature information, a plurality of atom vector representations and a plurality of edge vector representations, wherein the plurality of atom vector representations correspond to the plurality of atoms, respectively, and the plurality of edge vector representations correspond to the plurality of edges, respectively; performing, based on the fully connected graph, at least one aggregation on the plurality of atom vector representations and the plurality of edge vector representations to obtain a plurality of updated atom vector representations; and generating, based on the plurality of updated atom vector representations, a molecular vector representation of the molecule.

Abstract: A pig production method by using a detachable and washable wet curtain for intensive pig farming The detachable and washable wet curtain includes a wet curtain mounting frame (1), a spray tube (4), a plurality of rotatable reels (7) equivalent to sleeves, a reel (6) with a buckle, a wet curtain net (8), a reel (10) with a buckle, and a manual handle (9). The wet curtain is formed by a low-cost common material, namely a plastic net and the like, is convenient to mount, detach, and wash, and is not prone to making dust, scale, algae, mosquitos, and the like accumulate thereon, thus reducing the replacement cost and use cost. Therefore, a new cooling method based on the wet curtain is provided for pig farms in summer, and it is also suitable for other livestock farms in summer.

Abstract: The present disclosure provides a method for training a model, a method and an apparatus for generating molecules, and relates to the technical field of computer technology, particularly the technical field of artificial intelligence. The particular implementation may include: obtaining first molecular samples and second molecular samples; determining molecular difference information based on the first molecular samples and the second molecular samples; training an initial encoding module and an initial generation module based on the molecular difference information to obtain a target encoding module and a target generation module; and determining a molecule generation model based on the target encoding module and the target generation module.

Abstract: Embodiments of the present disclosure provide a method and electronic device for generating a molecule set and a storage medium thereof. The method obtains the first initialization molecule subset from the initialization molecule set with the pre-screening model; acquires the physical information of at least one initialization molecule in the first initialization molecule subset, and screens at least one initialization molecule based on the physical information to obtain the screened molecule set; acquires the biochemical experimental evaluation value of at least one molecule in the screened molecule set; and obtains the target molecule set based on the biochemical experimental evaluation value of at least one molecule.

Abstract: The present disclosure relates to a feedforward structure for controlling water level, in particular to a feedforward structure for controlling a steam drum water level in a steam turbine FCB test and a control method therefor. The feedforward structure includes a steam drum provided with a steam drum water level measurement sensor therein, a feed water pipe provided with a feed water flow measurement sensor therein, and a steam inlet pipe provided with a steam flow measurement sensor therein. A steam drum water level measurement value I in the steam drum water level measurement sensor is compared with a set value of the steam drum water level.

Abstract: Provided is a method for predicting protein-protein interaction. Also provided are an electronic device and a non-transitory computer readable storage medium.

Abstract: A molecular structure acquisition method, an electronic device and a storage medium, which relate to the field of artificial intelligence such as deep learning, are disclosed. The method may include: performing, for an initial seed, the following first processing: generating M molecular structures according to the seed, M being a positive integer greater than one; taking the M molecular structures as candidate molecular structures, and selecting some molecular structures from the candidate molecular structures as progeny molecular structures; and performing evolutionary learning on the progeny molecular structures, taking the progeny molecular structures after evolutionary learning as the seed, and repeating the first processing until convergence reaches an optimization objective, and when the convergence reaches the optimization objective, a newly selected molecular structure is taken as a desired molecular structure.

Abstract: The technical solution relates to the field of artificial intelligence technologies, such as machine learning technologies, natural language processing technologies, or the like. A plurality of training samples are collected, each of the plurality of training samples includes information of a known training target protein, information of two training drugs, and a real difference between affinities of the two training drugs for the known training target. The ranking learning model is trained with the plurality of training samples, such that the ranking learning model learns a capability of predicting a magnitude relationship between the affinities of the two training drugs for the known training target protein in each of the plurality of training samples.

Abstract: A method and apparatus for training a compound property prediction model, a device, a storage medium and a program product. A implementation of the method comprises: acquiring an unannotated compound data set; pre-training a graph neural network using the unannotated compound data set to obtain a pre-trained graph neural network; acquiring a plurality of annotated compound data sets, each annotated compound data set being annotated with one kind of compound property; and performing multi-task training on the pre-trained graph neural network using the plurality of annotated compound data sets, to obtain a compound property prediction model, the compound property prediction model being used to predict a plurality kinds of properties of a compound.

Abstract: Embodiment of the disclosure provide a method and apparatus for generating information. The method includes: acquiring vectors of a plurality of users, the vector being used to characterize behavior habits of the users; inputting the vectors of the plurality of users and push information pushed by a push system to the plurality of users into a feedback information generating model established in advance, to generate the feedback information of the plurality of users for the push information, wherein the feedback information generating model is used to characterize a corresponding relationship between the vectors, the push information and the feedback information; and generating an evaluation report of the push system based on the feedback information.

Abstract: The present disclosure discloses an affinity prediction method and apparatus, a method and apparatus for training an affinity prediction model, a device and a medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, smart medical technologies, or the like. An implementation includes: collecting a plurality of training samples, each training sample including information of a training target, information of a training drug and a test data set corresponding to the training target; and training an affinity prediction model using the plurality of training samples. In addition, there is further disclosed the affinity prediction method. The technology in the present disclosure may effectively improve accuracy and a training effect of the trained affinity prediction model.

Abstract: A method for predicting a compound property, apparatuses, an electronic device, a computer readable storage medium, and a computer program product are provided. The method includes: for each first sample compound of first sample compounds, acquiring spatial structure information of a spatial structure formed by atoms and chemical bonds that constitute the first sample compound; training, using the first sample compounds as input samples and pieces of corresponding spatial structure information as output samples, to obtain a spatial structure prediction model; and continuing training, using second sample compounds as input samples and pieces of corresponding property information as output samples, to obtain the compound property prediction model on the basis of the spatial structure prediction model.

Abstract: The present disclosure relates to a feedforward structure for controlling water level, in particular to a feedforward structure for controlling a steam drum water level in a steam turbine FCB test and a control method therefor. The feedforward structure includes a steam drum provided with a steam drum water level measurement sensor therein, a feed water pipe provided with a feed water flow measurement sensor therein, and a steam inlet pipe provided with a steam flow measurement sensor therein. A steam drum water level measurement value I in the steam drum water level measurement sensor is compared with a set value of the steam drum water level.

Abstract: The present application discloses an optimizer learning method and apparatus, an electronic device and a readable storage medium, which relates to the field of deep learning technologies. An implementation solution adopted by the present application during optimizer learning is: acquiring training data, the training data including a plurality of data sets each including neural network attribute information, neural network optimizer information, and optimizer parameter information; and training a meta-learning model by taking the neural network attribute information and the neural network optimizer information in the data sets as input and taking the optimizer parameter information in the data sets as output, until the meta-learning model converges. The present application can implement self-adaptation of optimizers, so as to improve generalization capability of the optimizers.

Abstract: A method and apparatus for optimizing a recommendation system, a device and a computer storage medium are described, which relates to the technical field of deep learning and intelligent search in artificial intelligence. A specific implementation solution is: taking the recommendation system as an agent, a user as an environment, each recommended content of the recommendation system as an action of the agent, and a long-term behavioral revenue of the user as a reward of the environment; and optimizing to-be-optimized parameters in the recommendation system by reinforcement learning to maximize the reward of the environment. The present disclosure can effectively optimize long-term behavioral revenues of users.

Abstract: Embodiments of the present disclosure provide a method and a device for predicting a box office trend of a film, a device and a storage medium. The method includes acquiring in real time a plurality of dynamic factor data of each of various films to be shown, in which, the dynamic factor data represents a factor that influences box office of the film; after a film in the various films is shown, incrementally updating a pre-trained box office prediction model by using box office data and the plurality of dynamic factor data of the film; and according to a preset period, predicting a box office trend of a target film to be predicted in the various films by using a box office prediction model incrementally updated in each preset period and the plurality of dynamic factor data of the target film, to obtain a plurality of prediction results.

Abstract: A pig production method by using a detachable and washable wet curtain for intensive pig farming. The detachable and washable wet curtain includes a wet curtain mounting frame (1), a spray tube (4), a plurality of rotatable reels (7) equivalent to sleeves, a reel (6) with a buckle, a wet curtain net (8), a reel (10) with a buckle, and a manual handle (9). The wet curtain is formed by a low-cost common material, namely a plastic net and the like, is convenient to mount, detach, and wash, and is not prone to making dust, scale, algae, mosquitos, and the like accumulate thereon, thus reducing the replacement cost and use cost. Therefore, a new cooling method based on the wet curtain is provided for pig farms in summer, and it is also suitable for other livestock farms in summer.

Abstract: A method and apparatus for building a route time consumption estimation model and a method and apparatus for estimating a route time consumption are described. A specific implementation solution comprises: obtaining training data from user trajectory data; obtaining the route time consumption estimation model by using the training data. The route time consumption estimation model comprises: a road segment subnetwork configured to obtain vector representations of road segments respectively based on the road segments included by the route and their context. The integration subnetwork determines an estimated time consumption on the route according to a feature representation of the time information, vector representations of the road segments and road condition feature representations of the road segments included by the route.