Abstract: A method includes exposing template polynucleotide strands, sequencing primers, and polymerase to flows of nucleotide species; obtaining a series of measured intensity values and randomly selecting a training subset therefrom; generating series of base calls using a base caller and aligning the series of base calls to a reference genome using an aligner; determining intensity value thresholds and parameters of a linear transformation corresponding to different homopolymer lengths and nucleotide species; generating series of base calls corresponding to the series of measured intensity values using at least some of the parameters of a linear transformation; and recalibrating the series of base calls corresponding to the plurality of series of measured intensity values using at least some of the intensity value thresholds.

Abstract: Output sentences of a primary large language model is provided to a criteria model including a second large language model. The criteria model compares the output to a reference source. As a result of comparing, the criteria model generates a first data structure including a first vector. The first vector stores, an evaluation of the output as being consistent or inconsistent with the reference source, and a corresponding reason for the evaluation. The criteria model identifies an inconsistent sentence, in the sentences, that is inconsistent with the reference source. The method also includes rewriting, by a reason improver model including a third large language model, the inconsistent sentence into a consistent sentence. The consistent sentence is consistent with the reference source. The output is modified by replacing the inconsistent sentence in the sentences with the consistent sentence. Modifying generates a modified output. The method also includes returning the modified output.

Abstract: Providing an output of a primary large language model to a criteria model including a second large language model. The criteria model compares each of the sentences to a reference source and generates a first data structure including a first vector. The first vector stores, for each of the sentences, a corresponding evaluation of a given sentence as being consistent or inconsistent with the reference source, and a corresponding reason for the corresponding evaluation of the given sentence. The first data structure is provided to a converter model including a third large language model. The converter model converts the first data structure to a second data structure. The second data structure includes a second vector storing scores indicating a corresponding consistency value for each of the sentences. A metric, indicating an overall consistency of the output with respect to the reference source, is generated from the second data structure.

Abstract: A strain for high-yield production of milbemycin and use thereof is disclosed. In the present invention, an original strain MI-W1-2017001 is subjected to mutation breeding by an atmospheric and room temperature plasma mutation method to obtain a high-yield strain HZMRYB-5. The fermentation level of the strain is improved by about 70% compared with the original strain, and the fermentation level is further improved by about 35% through optimization of a fermentation process and a formulation, so that the fermentation cost of the product is further reduced, and requirements for the industrial fermentation level are met. On the basis of optimizing the fermentation process, the ratio of A3 and A4 in a fermentation liquid is nearly 3:7 through mixed fermentation of the strain MI-W1-2017001 and the strain HZMRYB-5, so that market demands are greatly met, the process steps of separation, purification and then mixing are reduced.

Abstract: Systems and methods are disclosed for detecting hallucinations in large language models (LLMs). An example method includes receiving a first prompt for submission to the first LLM, generating, using the first LLM, a plurality of semantically equivalent prompts to the first prompt, generating, using the first LLM, a first response to the first prompt and a plurality of second responses to the plurality of semantically equivalent prompts, generating, using a second LLM, a plurality of third responses to the semantically equivalent prompts, generating a semantic consistency score for the first response based at least in part on the first prompt, the plurality of semantically equivalent prompts, the plurality of second responses, and the plurality of third responses, and determining whether or not the first response is an accurate response to the first prompt based at least in part on the semantic consistency score.

Abstract: The present disclosure provides an amplification primer for sequencing library construction comprising a primer sequence fragment complementary to a target fragment and a base G ligated to the 5? end of the primer sequence fragment, wherein the amplification primer and the target fragment are not complementary at the base G. Further disclosed are a method for constructing a sequencing library, a sequencing library, a kit for constructing a sequencing library, a mutant site detection kit, a chromosome ploidy detection kit, a gene fusion detection kit, and a sequencing method.

Abstract: Augmented Denoising Diffusion Implicit Models (“DDIMs”) using a latent trajectory optimization process can be used for image generation and manipulation using text input and one or more source images to create an output image. Noise bias and textual bias inherent in the model representing the image and text input is corrected by correcting trajectories previously determined by the model at each step of a diffusion inversion process by iterating multiple starts the trajectories to find determine augmented trajectories that minimizes loss at each step. The trajectories can be used to determine an augmented noise vector, enabling use of an augmented DDIM and resulting in more accurate, stable, and responsive text-based image manipulation.

Abstract: A glass part includes a substrate and a texture reinforcement layer disposed on at least one surface of the substrate. The texture reinforcement layer includes a plurality of reinforcement units. Cross sections of an outer surface of each reinforcement unit in at least a horizontal direction and a vertical direction are in a hyperbolic shape or a parabolic shape.

Abstract: A method including receiving a user input from a user device. The method also includes generating test inputs including the user input and modified inputs. The user input is processed with a rephrasing model to form the modified inputs. The method also includes executing a test model to generate test outputs, including an original test output and modified test outputs, from processing the test inputs. The method also includes generating similarity scores by performing similarity comparisons among the test outputs. The method also includes determining a model confidence from the similarity scores. The method also includes routing the user input responsive to the model confidence satisfying or failing to satisfy a confidence threshold.

Abstract: Some embodiments relate to a photothermal-curing resin composition, and a preparation method therefor and the use thereof. The resin composition includes a combination of specific parts of a prepolymer, a monomer, a photoinitiator, a thermal initiator, fumed silica and a glass powder. By means of adding specific parts of the photoinitiator in conjunction with the thermal initiator, the completion rate of a curing reaction is increased, the resin composition is more thoroughly cured, and the residual monomer rate after curing is lower, and specific parts of the fumed silica and barium-containing glass powder are further added as fillers, such that the hardness, strength and wear resistance of the resin composition are effectively enhanced; therefore, a product resulting from the 3D printing of the finally obtained resin composition completely meets the requirements for being used as temporary crowns and bridges.

Abstract: Aspects of the present disclosure provide techniques for active multifidelity machine learning. Embodiments include selecting, based on one or more criteria, a first subset of unlabeled training data for manual review and a second subset of unlabeled training data for providing to a pre-trained machine learning model for automated labeling. Embodiments include receiving manual label data for the first subset of unlabeled training data. Embodiments include providing inputs to the pre-trained machine learning model based on a subset of the manual label data and the second subset of training data. Embodiments include receiving, as outputs from the pre-trained machine learning model, automated label data for the second subset of unlabeled training data. Embodiments include generating a training data set for a target machine learning model based on the set of unlabeled training data, the manual label data, and the automated label data.

Abstract: A method includes receiving a user input including natural language text. The method also includes generating modified inputs from the user input. The method also includes executing a machine learning model on the modified inputs to generate model outputs. The method also includes sampling the model outputs using a statistical sampling strategy to generate sampled model outputs. The method also includes clustering the sampled model outputs into clusters. The method also includes generating a confidence metric of the clusters. The method also includes routing, automatically in a computing system, the user input based on whether the confidence metric satisfies a threshold value.

Abstract: Augmented Denoising Diffusion Implicit Models (“DDIMs”) using a latent trajectory optimization process can be used for image generation and manipulation using text input and one or more source images to create an output image. Noise bias and textual bias inherent in the model representing the image and text input is corrected by correcting trajectories previously determined by the model at each step of a diffusion inversion process by iterating multiple starts the trajectories to find determine augmented trajectories that minimizes loss at each step. The trajectories can be used to determine an augmented noise vector, enabling use of an augmented DDIM and resulting in more accurate, stable, and responsive text-based image manipulation.

Abstract: A Low Earth orbit (LEO) satellite congestion control routing method is disclosed, including: Step 1: A LEO satellite periodically detecting queue lengths of a plurality of ports and informing a Geostationary Earth Orbit (GEO) satellite of queue lengths of congested ports in the plurality of ports; Step 2: The GEO satellite sending the queue lengths of congested ports to a ground station, and a computing center of the ground station calculating link status weights according to the queue lengths of congested ports; Step 3: According to the link status weights, the computing center of the ground station determining a congestion area; Step 4: The computing center of the ground station calculating a routing table of satellites in the congestion area, and the ground station sending the routing table to a LEO satellite inside the congestion area; Step 5: The LEO satellite in the congestion area receiving the routing table, and performing end-to-end transmission of data packets to a destination LEO satellite accordin

Abstract: A glass substrate and a method for preparing the same, and an electronic device are provided. Two opposite sides of the glass substrate respectively have a first surface compressive stress layer and a second surface compressive stress layer. A surface compressive stress of the first surface compressive stress layer is greater than a surface compressive stress of the second surface compressive stress layer, and a depth of the first surface compressive stress layer is less than a depth of the second surface compressive stress layer.

Abstract: Systems and methods are disclosed for model based document image enhancement. Instead of requiring paired dirty and clean images for training a model to clean document images (which may cause privacy concerns), two models are trained on the unpaired images such that only the dirty images are accessed or only the clean images are accessed at one time. One model is a first implicit model to translate the dirty images from a source space to a latent space, and the other model is a second implicit model to translate the images from the latent space to clean images in a target space. The second implicit model is trained based on translating electronic document images in the target space to the latent space. In some implementations, the implicit models are diffusion models, such as denoising diffusion implicit models based on solving ordinary differential equations.

Abstract: Disambiguity in large language models (LLMs) includes receiving an original query in a user interface, generating an ambiguity query from the original query, and sending, via an application programming interface (API) of an LLM, the ambiguity query to the LLM. The ambiguity query includes the original query and training the LLM to recognize ambiguities. The method further includes receiving, via the API and responsive to the ambiguity query, a binary response and detecting, based at least in part on the binary response, the original query as ambiguous. Disambiguity may include detecting an ambiguity location in the original query using perturbed queries and the LLM.

Abstract: The present disclosure provides a ceramic material for improving surface adhesion of dental zirconia, a restorative material and a preparation method and a adhesion method thereof. The ceramic material includes SiO2, B2O3, GeO2, Al2O3, Li2O, Nb2O5, Na2O, ZrO2, P2O5, and a rare earth oxide. In the present disclosure, the ceramic material includes GeO2.

Abstract: The present disclosure discloses a lithium disilicate glass-ceramic with high strength and high transparency and a preparation method and use thereof. A raw material composition of the lithium disilicate glass-ceramic comprises: 63-75 wt % of SiO2, 13-18 wt % of Li2O, 1-6 wt % of Al2O3, 1-10 wt % of K2O, 2-6 wt % of P2O5, 0-4 wt % of an additive and 0-10 wt % of a colorant; a main crystal phase of the lithium disilicate glass-ceramic is lithium disilicate crystals, and an impurity phase of the lithium disilicate glass-ceramic is any one or a combination of at least two selected from the group consisting of lithium metasilicate, lithium phosphate and quartz; the lithium disilicate crystal has a size larger than 700 nm and a length-diameter ratio not less than 3.

Abstract: Provided are a fluorescent glass ceramic with high transparency and a preparation method and use thereof. The fluorescent glass ceramic includes the following raw materials by mass percentage: 63 wt % to 70 wt % of SiO2, 13 wt % to 16 wt % of Li2O, 1 wt % to 6 wt % of Al2O3, 1 wt % to 10 wt % of K2O, 2 wt % to 6 wt % of P2O5, 0.5 wt % to 3.5 wt % of CeO2, 0 wt % to 4 wt % of an additive, 1 wt % to 4 wt % of a lanthanide oxide with an atomic number of 59 to 71, and 0 wt % to 8 wt % of a colorant. The fluorescent glass ceramic has a lithium metasilicate crystal as a principal crystalline phase, and the lithium metasilicate crystal has a layered or plate-like structure and a grain size of 0.1 ?m to 1.5 ?m.