Abstract: The present disclosure provides an automatic production line for manufacturing and processing plant protein meat, and belongs to the field of application of food equipment. The automatic production line for manufacturing and processing the plant protein meat comprises: an extrusion-expansion machine, plant protein meat raw material blocks, a bearing plate, a first manipulator, a storage rack, a first conveying device, a second manipulator, a second conveying device, a main console, a third conveying device, a shredding device, a seasoning adding and mixing device, a third manipulator, a recycling rack, a storage cabinet and a secondary console.

Abstract: The present disclosure relates to a method for improving the yield and production intensity of Gluconobacter oxydans (G. oxydans) sorbose, and belongs to the technical field of fermentation engineering. By knocking out genes related to formation of D-sorbitol or L-sorbose metabolic by-products in G. oxydans, the formation of the by-products is reduced, and the efficiency of transforming D-sorbitol into L-sorbose is improved, thereby improving the yield and production intensity of L-sorbose. A recombinant strain G. oxydan-11 constructed by the present disclosure, compared with a control strain, has an L-sorbose transformation rate of 96.12%, which is 4.47% higher than that of a wild strain, has a production intensity of 14 g/L·h, which is 14.7% higher than that of the wild strain, and has a fructose by-product content of only 5.6 g/L, which is 45.6% lower than that of the wild strain.

Abstract: The present disclosure provides a method for extracting alpha-ketoglutarate and pyruvate simultaneously from microbial fermentation broth or enzyme transformation solution, which is related to the technical field of biological separation and extraction.

Abstract: Systems and methods are provided for facilitating the building and use of natural language understanding models. The systems and methods identify a plurality of tokens and use them to generate one or more pre-trained natural language models using a transformer. The transformer disentangles the content embedding and positional embedding in the computation of its attention matrix. Systems and methods are also provided to facilitate self-training of the pre-trained natural language model by utilizing multi-step decoding to better reconstruct masked tokens and improve pre-training convergence.

Abstract: Conversational virtual assistance for delivering relevant query solutions is provided. A virtual assistant system comprises various components associated with developing a knowledge database that can be searched for finding documents that fulfill the user's intent. The virtual assistant system further comprises components for receiving a query from a user, extracting entities for understanding the user's intent, and for searching a knowledge database for documents responsive to the query. When additional information is needed for determining more relevant results, a conversation strategy is determined, and a question is formulated for generating a conversation with the user for clarifying the user's intent, confirming a solution, or obtaining additional information. The user is enabled to provide a follow-up response that is related to a previously identified entity. The entity is edited in the query, and responses are refined responsive to the edited query.

Abstract: This document relates to training of machine learning models. One example method involves providing a machine learning model having one or more mapping layers. The one or more mapping layers can include at least a first mapping layer configured to map components of pretraining examples into first representations in a space. The example method also includes performing a pretraining stage on the one or more mapping layers using the pretraining examples. The pretraining stage can include adding noise to the first representations of the components of the pretraining examples to obtain noise-adjusted first representations. The pretraining stage can also include performing a self-supervised learning process to pretrain the one or more mapping layers using at least the first representations of the training data items and the noise-adjusted first representations of the training data items.

Abstract: Methods for configurable and proactive application diagnostics and recovery are performed by systems and devices. A diagnostics manager determines diagnostics packages corresponding to problems described in client device diagnostics requests. Session identifiers are generated and returned with diagnostics identifiers to clients which then provide the session identifiers and diagnostics identifiers to a service manager for session initiation. Diagnostics packages are located, retrieved, and provided back to the client by the service manager that invokes a client-side engine to execute diagnostics packages. Results are provided to the diagnostics system which determines additional packages to be executed by the engine during the same diagnostics session. Further, device-specific tokens are acquired by client devices which execute local diagnostic packages and acquire remote diagnostic packages for execution in the same session.

Abstract: Generally discussed herein are devices, systems, and methods for generating an embedding that is both local string dependent and global string dependent. The generated embedding can improve machine learning (ML) model performance. A method can include converting a string of words to a series of tokens, generating a local string-dependent embedding of each token of the series of tokens, generating a global string-dependent embedding of each token of the series of tokens, combining the local string dependent embedding the global string dependent embedding to generate an n-gram induced embedding of each token of the series of tokens, obtaining a masked language model (MLM) previously trained to generate a masked word prediction, and executing the MLM based on the n-gram induced embedding of each token to generate the masked word prediction.

Abstract: The invention provides a high-throughput screening system based on multi-manipulators, and belongs to the field of biotechnology and detection equipment. A high-throughput screening system based on multi-manipulators, comprises of the first manipulator, sampler, pipette, plate washer, microplate reader, the second manipulator, centrifuge, deep-well plate library, waste shallow-well plate barrel, shallow-well plate library, waste needle plate barrel, needle library, waste deep-well plate barrel, collection box. The present invention is a combination of microbiology and mechanics. The aim of the invention is to realize the automation and intelligentization of the high throughput screening experiment, effectively improve the experimental accuracy, reliability and efficiency. It contributes to the development of high throughput screening technology for microorganisms and drugs.

Abstract: Provided are a method for preparing a new type of recombinant anti-TNF-? chimeric monoclonal antibody and a use. The method comprises: designing and synthesizing the light chain and heavy chain of a CMAB008 antibody according to the preferred codon of a hamster; constructing a eukaryotic expression vector; transfecting an CHO-CR-GS ?/? host cell with a GS knockout; and cultivating the cell using a serum-free culture technique; isolating and purifying; and thereby obtaining a low immunogenic CMAB008 antibody.

Abstract: This document relates to training of machine learning models such as neural networks. One example method involves providing a machine learning model having one or more layers and associated parameters and performing a pretraining stage on the parameters of the machine learning model to obtain pretrained parameters. The example method also involves performing a tuning stage on the machine learning model by using labeled training samples to tune the pretrained parameters. The tuning stage can include performing noise adjustment of the labeled training examples to obtain noise-adjusted training samples. The tuning stage can also include adjusting the pretrained parameters based at least on the labeled training examples and the noise-adjusted training examples to obtain adapted parameters. The example method can also include outputting a tuned machine learning model having the adapted parameters.

Abstract: A system for training and deploying an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a memory storing machine readable instructions. The system may also comprise a processor to execute the machine readable instructions to receive a request via an artificial conversational entity. The processor may also transmit a response to the request based on a dialog tree generated from at least a model-based action generator and a memory-based action generator. The processor may further provide a training option to a user in the event the response is suboptimal. The processor may additionally receive a selection from the user via the training option. The selection may be associated with an optimal response.

Abstract: In some examples, iterative sampling based dataset clustering may include sampling a dataset that includes a plurality of items to identify a specified number of sampled items. The sampled items may be clustered to generate a plurality of clusters. Un-sampled items may be assigned from the plurality of items to the clusters. Remaining un-sampled items that are not assigned to the clusters may be identified. A ratio associated with the remaining un-sampled items and the plurality of items may be compared to a specified threshold. Based on a determination that the ratio is greater than the specified threshold, an indication of completion of clustering of the plurality of items may be generated.

Abstract: Methods, systems, apparatuses, and computer program products are provided for extracting an entity value from a sentence. An embedding set that may include one or more sentence embeddings is generated for at least part of a first sentence that is tagged to associate a first named entity in the sentence with an entity type. A plurality of candidate embeddings is also generated for at least part of a second sentence. The one or more sentence embeddings in the embedding set may be compared with each of the plurality of candidate embeddings, and a match score may be assigned to each comparison to generate a match score set. A particular match score of the match score set may be identified that exceeds a similarity threshold, and an entity value of the entity type may be extracted from the second sentence associated with the identified match score.

Abstract: Methods for configurable and proactive application diagnostics and recovery are performed by systems and devices. A diagnostics manager determines diagnostics packages corresponding to problems described in client device diagnostics requests. Session identifiers are generated and returned with diagnostics identifiers to clients which then provide the session identifiers and diagnostics identifiers to a service manager for session initiation. Diagnostics packages are located, retrieved, and provided back to the client by the service manager that invokes a client-side engine to execute diagnostics packages. Results are provided to the diagnostics system which determines additional packages to be executed by the engine during the same diagnostics session. Further, device-specific tokens are acquired by client devices which execute local diagnostic packages and acquire remote diagnostic packages for execution in the same session.

Abstract: This document relates to architectures and training procedures for multi-task machine learning models, such as neural networks. One example method involves providing a multi-task machine learning model having one or more shared layers and two or more task-specific layers. The method can also involve performing a pretraining stage on the one or more shared layers using one or more unsupervised prediction tasks.

Abstract: A system for training and deploying an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a memory storing machine readable instructions. The system may also comprise a processor to execute the machine readable instructions to receive a request via an artificial conversational entity. The processor may also transmit a response to the request based on a dialog tree generated from at least a model-based action generator and a memory-based action generator. The processor may further provide a training option to a user in the event the response is suboptimal. The processor may additionally receive a selection from the user via the training option. The selection may be associated with an optimal response.

Abstract: The present invention provides a production-line-type high-throughput screening system, which relates to the field of biotechnology and testing equipment. The system comprises of four manipulators, three parallel conveyor belts with fixed slots, 2-DOF slipway and fixed fixtures, 96-channel pipetting system, coloring device, oscillating mixing device, microplate reader, well plates loading platform and well plates recycling platform. Manual operation takes five minutes to detect one 96-well plate, while this system can handle 20 96-well plates per minute. It expands the number of screening targets, making the screening process more clearly and concisely and liberating manual labor. The system makes effective contributions to the development of microbial breeding technology.

Abstract: Provided are a method for preparing a new type of recombinant anti-TNF-? chimeric monoclonal antibody and a use. The method comprises: designing and synthesizing the light chain and heavy chain of a CMAB008 antibody according to the preferred codon of a hamster; constructing a eukaryotic expression vector; transfecting an CHO-CR-GS -/- host cell with a GS knockout; and cultivating the cell using a serum-free culture technique; isolating and purifying; and thereby obtaining a low immunogenic CMAB008 antibody.

Abstract: Generally discussed herein are devices, systems, and methods for visualization of user intent in accessing a virtual agent. A method can include receiving sentences from the respective interaction sessions, projecting the sentences to a higher-dimensional space to create respective higher-dimensional vectors, projecting the higher-dimensional vectors to a lower-dimensional space to create respective lower-dimensional vectors, the lower-dimensional space including either two dimensions or three dimensions, plotting the lower-dimensional vectors as respective points on a graph, encoding the respective points consistent with the respective intents determined to be associated with the sentences by an intent classifier to create encoded points, and generating a visual representation of the encoded points.