Abstract: This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.

Abstract: A method may include: receiving, by a computer program executed by an electronic device, an original sample to process; extracting, by the computer program, a feature from the original sample using a trained neural network, wherein the neural network may be trained to extract features from samples; calculating, by the computer program, a probability of substituting the original sample with each sample of a plurality of samples in a dataset; substituting, by the computer program, the original sample with a sample in the dataset based on the calculated probability; and returning, by the computer program, the substituted sample, wherein sensitive attributes of the original sample cannot be inferred from the substituted sample, while useful attributes of the original sample may be inferred from the substituted sample.

Abstract: A biodegradable polymer usable in fishing gear and that biodegrades in aquatic environments. The polymer includes a polymer backbone that has monomeric units that are susceptible to hydrolytic degradation, and a plurality of pH responsive moieties. Each pH responsive moiety is grafted to a respective one of the monomeric units. The pH responsive moieties are relatively hydrophilic when exposed to an aqueous solution of a pro-biodegradation pH range to facilitate hydrolytic degradation of the monomeric units, and are relatively hydrophobic when removed from the aqueous solution of the pro-biodegradation pH range, to protect the monomeric units from hydrolytic degradation.

Abstract: A monitoring apparatus adapted to be mounted to a trap device, includes an adapter module and a monitor module. The adapter module is attached to the trap device. The monitor module includes a casing separably attached to the adapter module, and a core system disposed in the casing. The core system includes a main board and a connectivity module. The connectivity module is coupled to the main board for communication with an external device.

Abstract: A method and system for mitigating predictive multiplicity in a gradient boosting model (GBM). The method includes generating an empirical parameter set based on an approximation search resulting in a subset that includes candidates of at least one weak learner model (WLM) from a predetermined set of WLMs and training iteratively the empirical parameter set to derive a group filtered from the subset based on at least one from among a model selection (MS) technique and an intermediate ensembles (IE) technique. The method also includes selecting sequentially the at least one WLM from the derived group based on the at least one from among the MS technique and the IE technique; and generating the GBM based on a compilation of the sequentially selected at least one WLM, wherein the generated GBM operates below a minimum predefined disagreement threshold related to assessing predictive multiplicity, thereby mitigating the predictive multiplicity.

Abstract: Systems and methods for detecting human-generated text and machine-generated text in mixed text are disclosed. A computer program receives a dataset of human-generated text and generates a synthetic mixed text dataset by randomly replacing portions of the human-generated text with machine-generated text. The program evaluates the synthetic mixed text dataset at multiple levels, each representing a different fraction of machine-generated text, and computes a first text discriminator score for each level. Upon receiving a text sample, the program computes a second text discriminator score, fits a kernel density estimate using a Gaussian kernel to model a conditional probability distribution, samples from a posterior distribution, and returns an interval representing the amount of machine-generated text in the sample.

Abstract: This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.

Abstract: A method may include: receiving a prompt and generated text from the LLM; computing an original token probability distribution for each token in the prompt and in the generated text; receiving a token position probability distribution for each token position in the generated text from the LLM; identifying keywords in the prompt; perturbing embedding vectors for the keywords used by the LLM by adding noise to the embedding vectors; computing a perturbed probability distribution for the perturbed embedding vectors by providing the perturbed embedding vectors as an input to a neural network used by the LLM, wherein the neural network returns a perturbed token probability distribution; evaluating a divergence between the original token probability distribution and the perturbed token probability distribution; identifying semantically meaningful tokens in the generated text; calculating a mean of divergences for the semantically meaningful tokens; and classifying the LLM based on the mean of divergences.

Abstract: A recognition network is trained for a selected video frame at a desired highest precision using back-propagation and a policy network is trained using back-propagation from the trained recognition network. The recognition network is trained at a lower precision specified by a policy recommended for the selected video frame by the trained policy network. A frame of a given video is inputted to the trained policy network for determination of a precision policy for processing the frame. Video inferencing is performed utilizing the trained policy network and the trained recognition network based on the precision policy.

Abstract: Methods, systems, and computer program products for translating text using generated visual representations and artificial intelligence are provided herein. A computer-implemented method includes generating a tokenized form of at least a portion of input text in a first language; generating at least one visual representation of at least a portion of the input text using a first set of artificial intelligence techniques; generating a tokenized form of at least a portion of the at least one visual representation; and generating an output including a translated version of the input text into at least a second language by processing, using a second set of artificial intelligence techniques, at least a portion of the tokenized form of the at least a portion of the input text and at least a portion of the tokenized form of the at least a portion of the at least one visual representation.

Abstract: A method may include: receiving a dataset comprising a plurality of samples and a loss function; training a first number first machine learning models using the dataset comprising, wherein each of the first machine learning models has a similar performance; selecting one of the first machine learning models with a smallest loss; computing a residual for each of the plurality of samples using the one first machine learning model; defining a new dataset comprising the plurality of samples and the residual for each samples; training the first machine learning model with the new dataset; generating a second plurality of machine learning models by repeating the selecting, the computing, the defining, and training for a number of boosting iterations; selecting a subset of the second plurality of machine learning model models having a specified property; and deploying the subset of second machine learning models to a downstream task.

Abstract: This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.

Abstract: An example method of producing a microelectromechanical system (MEMS) package, the method comprising: applying first epoxy layers to a first substrate, at least one of the first epoxy layers coupled to a second substrate; applying a first post gel heat treatment to the first epoxy layers; after applying the first post gel heat treatment to the first epoxy layers, applying second epoxy layers to the second substrate and to the first epoxy layers; and applying a second post gel heat treatment to the first epoxy layers and the second epoxy layers.

Abstract: The present disclosure provides methods and systems for personalized genetic testing of disease in a subject, in particular for identifying and tracking genetic mutations identified in an individual subject to monitor for cancer or for the spread or recurrence of the disease. In some embodiments, custom assays, including custom panels designed to target sequence data corresponding to both subject-specific loci and other loci known for cancer-causing or therapy resistance mutations, are designed based upon the sequencing of a screening biopsy sample. Such custom assays are then run on subsequently obtained tissue samples, such as tissue obtained from a surgical resection of a primary or metastatic tumor or from a lymph node biopsy. The subsequently obtained tissue samples can be taken from the subject at various time points after an initial screening biopsy to further allow for extended monitoring of the subject for spread or recurrence of the disease.

Abstract: This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.

Abstract: An example method of producing a microelectromechanical system (MEMS) package, the method comprising: applying first epoxy layers to a first substrate, at least one of the first epoxy layers coupled to a second substrate; applying a first post gel heat treatment to the first epoxy layers; after applying the first post gel heat treatment to the first epoxy layers, applying second epoxy layers to the second substrate and to the first epoxy layers; and applying a second post gel heat treatment to the first epoxy layers and the second epoxy layers.

Abstract: Systems and methods are provided for analysis of pathology data. Either a input data representing a pathology or a search query is received as an input and a first set of tokens is generated from the one of the input data representing a pathology and the search query from the input. The first set of tokens is matched to a second set of tokens at a multimodal fusion model trained on a pretraining dataset complied from a plurality of pathology-related sources. An output is provided based on the second set of tokens.

Abstract: Systems and methods are provided for providing natural language decision support for pathology. A lower-dimensionality representation of each of a set of received pathology image is generated and a first set of tokens is generated from the representations of the set of pathology images by projecting the lower-dimensionality representations of the received pathology images to a same dimension as an embedding space of a large language model for text tokens or through multimodal blocks added to the large language model such as cross-attention. The large language model is trained on an instruction dataset complied from a plurality of pathology-related sources. A second set of tokens associated with a natural language prompt is received at the large language model. A response is determined from the first set of tokens and the second set of tokens at the large language model.

Abstract: This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.

Abstract: A method may include: accepting, by a data transformation module, an original dataset as input to a first and a second neural network and outputting a transformed dataset; accepting, by a sensitive attribute suppression module, the transformed dataset as input to a third neural network and calculating a sensitive attribute suppression loss; accepting, by an annotated useful attribute preservation module, the transformed dataset as input to a fourth neural network and calculating a useful attribute preservation loss; accepting by a generic feature suppression module, parameters of a distribution of a latent variable from the first neural network and calculating, for an unannotated generic attribute, a generic feature suppression loss; combining the sensitive attribute suppression loss, the useful attribute preservation loss, and the generic feature suppression loss into a total loss; and training the first neural network and the second neural network with the total loss.