Abstract: A zinc aluminium silicate nanoparticles/granular red mud (ZAS/GRM) composite material of Zn2+-modified industrial waste red mud and a preparation method and application thereof are disclosed, belonging to the technical field of adsorbent preparation. The industrial waste red mud is used as a raw material to prepare Zn2+-modified red mud for ZAS/GRM adsorbent.

Abstract: Systems and methods for controlling self-driving robots are provided. Data from a plurality of sensors, associated with a robotic device, is received by a plurality of neural network models. Each neural network model of the plurality of neural network models receives a subset of the data from the plurality of sensors. The plurality of neural network models generate, based on the data from the plurality of sensors, a plurality of outputs. Each output of the plurality of outputs is generated by a particular neural network model of the plurality of neural network models. Each output corresponds to a respective subset of data received by the particular neural network model of the plurality of neural network models. Thereafter, an output from the plurality of outputs is selected, where the output includes data for controlling the robotic device. The robotic device is then controlled based on the output.

Abstract: Techniques are described for scenario data generation. An example method can include processing location-specific sensor information of a traffic accident at a location on a road network, the location-specific sensor information received from a sensor arranged on an infrastructure at the road network. The method can further include determining a location-specific element based on the location-specific sensor information. The method can further include determining a location-specific contributing factor of the traffic accident based on the location-specific element. The method can further include generating a location-specific model for simulating the traffic accident based on the location-specific contributing factor. The method can further include determining control instructions for a location-specific driving maneuver for an autonomous vehicle to mitigate a future traffic accident based on the location-specific model.

Abstract: A model training method, a model training platform, an electronic device and a storage medium are provided, which can be used in the field of artificial intelligence, particularly the fields of natural language processing and deep learning. The model training method includes: receiving an input; determining, based on the input, a user-oriented prefabricated function; determining, based on the input, a model training function; determining, based on the input, a pre-trained model; determining, based on the input, a network structure associated with the pre-trained model so as to support use of the pre-trained model; training, based on the input, the model by using the prefabricated function, the model training function, and the pre-trained model; and providing an output associated with a trained model.

Abstract: Disclosed herein are a pharmaceutical composition comprising an inhibitor of Olfr164 for the prevention or treatment of sepsis, a method for the prevention or treatment of sepsis, a use of Olfr164 as a marker for selecting sepsis therapeutic agents, a screening method for sepsis therapeutics using same, and a transgenic animal in which Olfr164 is knocked out. The composition comprising Olfr164 inhibitor can effectively prevent or treat sepsis. Additionally, sepsis therapeutics can be effectively screened by comparing the expression level or activity of Olfr164 before and after administering the candidate substance.

Abstract: The present disclosure discloses a base editor and the use thereof. The present disclosure provides a nucleic acid base editor, specifically a base editor which is not based on CRISPR technology. The base editor comprises a sequence-specific DNA binding protein, a nickase, an exonuclease and a base-specific deaminase. This base editor is single-strand-specific, and as compared with conventional base editors, the base editor of the present disclosure has wide applicability in cells and is capable of functioning in the nucleus as well as in mitochondrial DNA and/or chloroplast DNA. This base editor has the characteristics of achieving base editing products with high purity and resulting in few indel byproducts while realizing efficient base editing, which is conducive to being used as an efficient and safe gene editing tool.

Abstract: In one aspect, a method of guarding an automated software includes generating, by a first language model, a training set of rule-violating data, generating, by the first language model, a training set of contrastive examples by altering the rule-violating data into non-violating data, training a guardrail machine learning model using the generated training sets, generating, with an automated software, an output based on a user input, monitoring with the trained guardrail machine learn model whether the generated output violates a rule, and preventing the automated software from transmitting to the user the generated output that violates a rule.

Abstract: Provided is a large language model training method, an electronic device and a storage medium, relating to the field of artificial intelligence technologies, and in particular, to the fields of deep learning, natural language processing and large model. The method includes: performing dimension reduction parameter fusion on a two-dimensional parameter matrix on each channel in each network layer in a first large language model, respectively, to obtain a second large language model; performing layer reduction parameter fusion on network layers in the second large language model based on a three-dimensional parameter matrix of each network layer in the second large language model to obtain a third large language model; and training the third large language model to obtain a target large language model under the condition that the target loss function determined based on the first and third large language models meets a preset first function condition.

Abstract: A task execution method for a large model relates to fields of artificial intelligence, deep learning and large model technologies, and includes executing attention tasks in a task group to be fused using a target computing unit to obtain attention features, where the attention task corresponds to a weighted matrix to be fused, the weighted matrix to be fused is obtained by weighting a matrix to be fused using a weight; obtaining a processing result according to the attention features; determining a loss information according to the processing result; and weighting and fusing matrices to be fused using the target computing unit according to weights for the task group to be fused if the loss information converges, to obtain a fusion matrix for a target task group, where a target task in the target task group is executed by the target computing unit according to the fusion matrix.

Abstract: Provided is a model training method, a model reasoning method, an electronic device, and a storage medium, relating to the field of data processing, and especially to the technical fields of artificial intelligence, big data, deep learning and large models. The model training method includes: folding an initial token sequence for training a model based on a folding feature value for folding a token sequence to obtain at least a first token sequence subjected to the folding, wherein the initial token sequence represents a token sequence composed of T1 tokens, and the first token sequence has a sequence length less than that of the initial token sequence; and inputting at least the first token sequence into a preset model to train the preset model so as to obtain a target model.

Abstract: A multimodal data generation method is provided. The method includes: inputting a query data sequence into a multimodal model, to obtain a plurality of tokens in a response data sequence, where a current token is generated through the following operations: inputting the query data sequence and a current response data sequence into the multimodal model, so that the multimodal model generates the current token based on the query data sequence and the current response data sequence, in response to determining that the current token belongs to a first data modality; or inputting the query data sequence and a current response data sequence into the multimodal model, so that the multimodal model denoises an initial token sequence based on the query data sequence and the current response data sequence, to generate a result token sequence, in response to determining that the current token belongs to a second data modality.

Abstract: The present application discloses an enzymatic electrode system and its applications. The enzymatic electrode system comprises a working electrode, a counter electrode, and an optional reference electrode. The working electrode includes an electrode support substrate and a conductive substrate located at the top of the electrode support substrate. The surface or interior of the conductive substrate contains oxidoreductases, coenzyme reductases, and optional coenzymes. This application utilizes a comprehensive enzymatic electrode system incorporating oxidoreductases, coenzyme reductases, and coenzymes for the detection of specific substances in samples, such as glucose, lactate, ketones, and the like.

Abstract: An expanding and shaping device for cervical spinal canal and a mounting device are provided, including the first fixing part, the second fixing part, and the fasteners. The passing-through direction of the fasteners is consistent with the direction of the incision. The fasteners are nailed along a coronal surface. The fasteners pass through the second fixing hole to connect the lateral mass.

Abstract: A hyaluronic acid derivative or a salt thereof, and a preparation method therefor and an application thereof. Hyaluronic acid is used as a matrix skeleton, and a methoxypolyethylene glycol epoxy derivative is used to modify hyaluronic acid to prepare a pegylated hyaluronic acid derivative. The skeleton of hyaluronic acid is wound by using polyethylene glycol linear macromolecules, so that a glycosidic bond facilitating enzymolysis is masked, a circulation duration of sodium hyaluronate in the body is prolonged, and the moisture retention and moisture lock effect can be better exerted. Meanwhile, polyethylene glycol and sodium hyaluronate, serving as artificially synthesized and naturally existing high polymer materials, both have good biocompatibility and biodegradability, and are combined, so that the advantages of the high polymer material can be better exerted.

Abstract: A curable rubber composition including a rubber, a plurality of ground particles, and a reactive surfactant represented by the formula: X—Y—Z where X is a reactive group capable of forming covalent links with the rubber during compounding or vulcanization, Y is a hydrophobic linkage, and Z is a polar group capable of forming self-assemblies via intermolecular interactions, and wherein the reactive surfactant is incompatible with the rubber and a method of making the same.

Abstract: A training method, an inference method, a device, an apparatus, and a medium for a deep learning model are provided. A first model includes a plurality of first parameters, a second model comprises a plurality of second parameters, which is initialized to parameter values of a plurality of target parameters selected from the plurality of first parameters. The training method includes: determining a target loss for both the first model and the second model; adjusting parameter values, including: in response to determining that the target loss indicates that the parameter values of at least part of the target parameters need to be adjusted, synchronously adjusting the parameter values of the corresponding second parameters; and in response to determining that the target loss indicates that the parameter values of at least part of the second parameters need to be adjusted, synchronously adjusting the parameter values of the corresponding target parameters.