Abstract: A retaining mechanism for a clevis hook assembly with an axial device that provides a retaining mechanism for a load pin. The axial device is disposed inside a first opening behind the load pin. The axial device may provide a retention mechanism to obstruct the load pin when the load pin is inserted into the first opening of one of the pair of opposed members and extends across a space into the first opening in the other opposed member.

Abstract: A system and method of at least partially forming reinforced packages. The method comprises moving a construct in a machine direction on a construct conveyor. The construct conveyor can comprise a primary lug belt with a primary lug, the construct can be disposed on the primary lug belt, and the moving the construct can comprise moving the primary lug belt in the machine direction to push the construct in the machine direction. The method further can comprise positioning a liner on the construct conveyor so that the liner at least partially extends over the construct and moving the liner in the machine direction on a secondary lug belt of the construct conveyor. The secondary lug belt can comprise a secondary lug and the moving the liner can comprise moving the secondary lug belt in the machine direction to push the liner in the machine direction.

Abstract: In one example, a system can execute a first machine-learning model to determine an overall classification for a textual dataset. The system can also determine classification scores indicating the level of influence that each token in the textual dataset had on the overall classification. The system can select a first subset of the tokens based on their classification scores. The system can also execute a second machine-learning model to determine probabilities that the textual dataset falls into various categories. The system can determine category scores indicating the level of influence that each token had on a most-likely category determination. The system can select a second subset of the tokens based on their category scores. The system can then generate a first visualization depicting the first subset of tokens color-coded to indicate their classification scores and a second visualization depicting the second subset of tokens color-coded to indicate their category scores.

Abstract: A system, method, apparatus, and computer program product for generating a cabling plan for a computing system are disclosed. A method may include accessing an elevation plan defining a respective mount position for each of a set of components of the computing system. The method may also include determining one or more port pairs to be used for interconnecting the plurality of components. The method may further include determining, for each port pair, a cable length to use to connect the port pair. The cable length may be determined based at least in part on mount positions defined by the elevation plan for the two components connected by the port pair. The method may additionally include generating a cabling plan defining the cable length to use to connect each respective port pair.

Abstract: Computerized pipelines can transform input data into data structures compatible with models in some examples. In one such example, a system can obtain a first table that includes first data referencing a set of subjects. The system can then execute a sequence of processing operations on the first data in a particular order defined by a data-processing pipeline to modify an analysis table to include features associated with the set of subjects. Executing each respective processing operation in the sequence to generate the modified analysis table may involve: deriving a respective set of features from the first data by executing a respective feature-extraction operation on the first data; and adding the respective set of features to the analysis table. The system may then execute a predictive model on the modified analysis table for generating a predicted value based on the modified analysis table.

Abstract: Computerized pipelines can transform input data into data structures compatible with models in some examples. In one such example, a system can obtain a first table that includes first data referencing a set of subjects. The system can then execute a sequence of processing operations on the first data in a particular order defined by a data-processing pipeline to modify an analysis table to include features associated with the set of subjects. Executing each respective processing operation in the sequence to generate the modified analysis table may involve: deriving a respective set of features from the first data by executing a respective feature-extraction operation on the first data; and adding the respective set of features to the analysis table. The system may then execute a predictive model on the modified analysis table for generating a predicted value based on the modified analysis table.

Abstract: A system trains a classification model. Text windows are defined from tokens based on a window size. A network model including a transformer network is trained with the text windows to define classification information. A first accuracy value is computed. (A) The window size is reduced using a predefined reduction factor value. (B) Second text windows are defined based on the reduced window size. (C) Retrain the network model with the second text windows to define classification information. (D) A second accuracy value is computed. (E) An accuracy reduction value is computed from the second accuracy value relative to the first accuracy value. When the computed accuracy reduction value is ?an accuracy reduction tolerance value, repeat (A)-(E) until the accuracy reduction value is <the accuracy reduction tolerance value. Otherwise, increase the window size, define final text windows based on the increased window size, and retrain the network model.

Abstract: A computing system receives a collection comprising multiple sets of ordered terms, including a first set. The system generates a dataset indicating an association between each pair of terms within a same set of the collection by generating co-occurrence score(s) for the first set. The system generates computed probabilities based on the co-occurrence score(s) for the first set. The computed probabilities indicate a likelihood that one term in a given pair of terms of the collection appears in a given set of the collection given that another term in the given pair of terms of the collection occurs. The system smoothes the computed probabilities by adding one or more random observations. The system generates one or more association indications for the first set based on the smoothed computed probabilities. The system outputs an indication of the dataset. Additionally, or alternatively, based on association measure(s), the system generates a virtual term.

Abstract: A system and method of at least partially forming reinforced packages. The method comprises moving a construct in a machine direction on a construct conveyor. The construct conveyor can comprise a primary lug belt with a primary lug, the construct can be disposed on the primary lug belt, and the moving the construct can comprise moving the primary lug belt in the machine direction to push the construct in the machine direction. The method further can comprise positioning a liner on the construct conveyor so that the liner at least partially extends over the construct and moving the liner in the machine direction on a secondary lug belt of the construct conveyor. The secondary lug belt can comprise a secondary lug and the moving the liner can comprise moving the secondary lug belt in the machine direction to push the liner in the machine direction.

Abstract: A system and method of at least partially forming reinforced packages. The method comprises moving a construct in a machine direction on a construct conveyor. The construct conveyor can comprise a primary lug belt with a primary lug, the construct can be disposed on the primary lug belt, and the moving the construct can comprise moving the primary lug belt in the machine direction to push the construct in the machine direction. The method further can comprise positioning a liner on the construct conveyor so that the liner at least partially extends over the construct and moving the liner in the machine direction on a secondary lug belt of the construct conveyor. The secondary lug belt can comprise a secondary lug and the moving the liner can comprise moving the secondary lug belt in the machine direction to push the liner in the machine direction.

Abstract: A computing system receives a collection comprising multiple sets of ordered terms, including a first set. The system generates a dataset indicating an association between each pair of terms within a same set of the collection by generating co-occurrence score(s) for the first set. The system generates computed probabilities based on the co-occurrence score(s) for the first set. The computed probabilities indicate a likelihood that one term in a given pair of terms of the collection appears in a given set of the collection given that another term in the given pair of terms of the collection occurs. The system smoothes the computed probabilities by adding one or more random observations. The system generates one or more association indications for the first set based on the smoothed computed probabilities. The system outputs an indication of the dataset. Additionally, or alternatively, based on association measure(s), the system generates a virtual term.

Abstract: Embodiments relate generally to systems and methods for filtering unwanted wavelengths from an IR detector. In some embodiments, it may be desired to remove or reduce the wavelengths absorbed by water, to reduce the effects of water on the detection of the target gas. In some embodiments, a filter glass may be used in the IR detector, wherein the filter glass comprises one or more materials that contain hydroxyls in their molecular structure, and wherein the spectral absorption properties of the filter glass are operable to at least reduce wavelengths of light absorbed by water from the optical, thereby reducing the IR detector's cross sensitivity to water.

Abstract: A computing system receives a collection comprising multiple sets of ordered terms, including a first set. The system generates a dataset indicating an association between each pair of terms within a same set of the collection by generating co-occurrence score(s) for the first set. The system generates computed probabilities based on the co-occurrence score(s) for the first set. The computed probabilities indicate a likelihood that one term in a given pair of terms of the collection appears in a given set of the collection given that another term in the given pair of terms of the collection occurs. The system smoothes the computed probabilities by adding one or more random observations. The system generates one or more association indications for the first set based on the smoothed computed probabilities. The system outputs an indication of the dataset. Additionally, or alternatively, based on association measure(s), the system generates a virtual term.

Abstract: A computing system receives a collection comprising multiple sets of ordered terms, including a first set. The system generates a dataset indicating an association between each pair of terms within a same set of the collection by generating co-occurrence score(s) for the first set. The system generates computed probabilities based on the co-occurrence score(s) for the first set. The computed probabilities indicate a likelihood that one term in a given pair of terms of the collection appears in a given set of the collection given that another term in the given pair of terms of the collection occurs. The system smoothes the computed probabilities by adding one or more random observations. The system generates one or more association indications for the first set based on the smoothed computed probabilities. The system outputs an indication of the dataset. Additionally, or alternatively, based on association measure(s), the system generates a virtual term.

Abstract: A non-dispersive photoacoustic gas detector includes an infrared light source, a first closed chamber, a first acoustic sensor in fluid communication with the first closed chamber, a second closed chamber, and a second acoustic sensor in fluid communication with the second closed chamber. The first closed chamber comprises a plurality of windows that are substantially transparent to infrared light from the infrared light source. The second closed chamber comprises at least one window that is substantially transparent to infrared light from the infrared light source, and the first closed chamber is arranged in series with the second closed chamber between the infrared light source and the second closed chamber.

Abstract: Recurrent neural networks (RNNs) can be visualized. For example, a processor can receive vectors indicating values of nodes in a gate of a RNN. The values can result from processing data at the gate during a sequence of time steps. The processor can group the nodes into clusters by applying a clustering method to the values of the nodes. The processor can generate a first graphical element visually indicating how the respective values of the nodes in a cluster changed during the sequence of time steps. The processor can also determine a reference value based on multiple values for multiple nodes in the cluster, and generate a second graphical element visually representing how the respective values of the nodes in the cluster each relate to the reference value. The processor can cause a display to output a graphical user interface having the first graphical element and the second graphical element.

Abstract: A system, method, apparatus, and computer program product for generating an elevation plan for a computing system are disclosed. A method may include accessing a build specification identifying components to be included in the computing system. The method may also include accessing a set of component placement rules defining rules for placing components within a rack. The method may further include generating an elevation plan defining a respective mount position for each of a subset of the components within one or more racks based on the set of component placement rules.

Abstract: Methods, processes and computer-program products are disclosed for use in a parallelized computing system in which representations of large sparse matrices are efficiently encoded and communicated between grid-computing devices. A sparse matrix can be encoded and stored as a collection of character strings wherein each character string is a Base64 encoded string representing the non-zero elements of a single row of the sparse matrix. On a per-row basis, non-zero elements can be identified by column indices and error correction metadata can be included. The resultant row data can be converted to IEEE 754 8-byte representations and then encoded into Base64 characters for storage as strings. These character strings of even very large-dimensional sparse matrices can be efficiently stored in databases or communicated to grid-computing devices.

Abstract: A system and method of at least partially forming reinforced packages. The method comprises moving a construct in a machine direction on a construct conveyor. The construct conveyor can comprise a primary lug belt with a primary lug, the construct can be disposed on the primary lug belt, and the moving the construct can comprise moving the primary lug belt in the machine direction to push the construct in the machine direction. The method further can comprise positioning a liner on the construct conveyor so that the liner at least partially extends over the construct and moving the liner in the machine direction on a secondary lug belt of the construct conveyor. The secondary lug belt can comprise a secondary lug and the moving the liner can comprise moving the secondary lug belt in the machine direction to push the liner in the machine direction.

Abstract: Recurrent neural networks (RNNs) can be visualized. For example, a processor can receive vectors indicating values of nodes in a gate of a RNN. The values can result from processing data at the gate during a sequence of time steps. The processor can group the nodes into clusters by applying a clustering method to the values of the nodes. The processor can generate a first graphical element visually indicating how the respective values of the nodes in a cluster changed during the sequence of time steps. The processor can also determine a reference value based on multiple values for multiple nodes in the cluster, and generate a second graphical element visually representing how the respective values of the nodes in the cluster each relate to the reference value. The processor can cause a display to output a graphical user interface having the first graphical element and the second graphical element.