Abstract: This disclosure enables various computing technologies for preserving signals for artificial neural networks when downsampling. These data science techniques can address various technological concerns and can be helpful for dealing with time series or non-fixed-length time spans or other forms of discretized, parsed, or tokenized data. Some of the data science techniques can enable a process that is technologically beneficial to a user dealing with temporal data sequences that contain multiple event types with differing frequencies. Some of the data science techniques can enable a speed improvement in terms of training an ANN or an accuracy improvement in terms of training an ANN. Some of the data science techniques can enable a technique that implements a series of pooling operations, including learnable pools, to preserve event presence after downsampling.

Abstract: This disclosure enables various computing technologies for various data science techniques for ameliorating negative impacts of signals that are sparse in various data series for trainings of ANN models. These data science techniques can be helpful for dealing with time series or non-fixed-length time spans or other forms of discretized, parsed, or tokenized data. Some of the data science techniques can enable a process that ameliorates a negative impact of a sparse signal on a learning performance of an ANN model. This amelioration can occur by adjusting an impact of a computed loss on a learning process of an ANN on a sample-by-sample basis in such a way as to reflect a probability that the ANN model has seen a signal for that sample.

Abstract: This disclosure enables various computing technologies for various data science techniques to preserve signals in data inputs with moderate to high levels of variances in data sequence lengths for artificial neural network model training. These data science techniques can be helpful for dealing with time series or non-fixed-length time spans or other forms of discretized, parsed, or tokenized data. Some technological effects of utilizing these data science techniques can be technologically equivalent to getting more relevant training data, which can allow a model of a neural network to be trained to a high level of accuracy.

Abstract: Supporting assemblies for supporting a suspended load and processes relating thereto are described. An exemplar supporting assembly includes: (i) an area-narrowing feature including a first inner surface defining a first aperture; (ii) a main tube including an interior-main surface defining a cavity having disposed therewithin the area-narrowing feature; (iii) an area-expanding feature including a second outer surface; and (iv) a telescoping rod including an outer-telescoping surface having disposed thereon the area-expanding feature. In an assembled state of the supporting assembly, the outer-telescoping surface slidably passes through and establishes a first frictional contact with the first inner surface, and the second outer surface slidably engages and establishes a second frictional contact with the interior-main surface.

Abstract: Some embodiments relate to techniques for receiving a query from a device, the query comprising content; determining based at least in part on the content of the query that an application is to be launched on the device; and causing the device to launch the application using at least some information determined from the content of the query. Some embodiments relate to techniques for receiving a free-form query from a user; transferring a representation of the query to at least one computer; and receiving from the at least one computer at least one instruction to launch an application on the device.

Abstract: Some embodiments relate to techniques for receiving a query from a device, the query comprising content; determining based at least in part on the content of the query that an application is to be launched on the device; and causing the device to launch the application using at least some information determined from the content of the query. Some embodiments relate to techniques for receiving a free-form query from a user; transferring a representation of the query to at least one computer; and receiving from the at least one computer at least one instruction to launch an application on the device.

Abstract: Some embodiments relate to techniques for performing a search for content, in which a user may issue a search query, and the search engine or engines to which that query is provided may be determined dynamically based on any of a variety of factors. For example, in some embodiments, the search engine or engines to which the query is provided may be determined based on the content of the search query, and/or auxiliary information such as the user's location, demographics, query history and/or browsing history.

Abstract: Some embodiments relate to techniques for performing a search for content, in which a user may issue a search query, and the search engine or engines to which that query is provided may be determined dynamically based on any of a variety of factors. For example, in some embodiments, the search engine or engines to which the query is provided may be determined based on the content of the search query, and/or auxiliary information such as the user's location, demographics, query history and/or browsing history.

Abstract: Some embodiments relate to techniques for receiving a query from a device, the query comprising content; determining based at least in part on the content of the query that an application is to be launched on the device; and causing the device to launch the application using at least some information determined from the content of the query. Some embodiments relate to techniques for receiving a free-form query from a user; transferring a representation of the query to at least one computer; and receiving from the at least one computer at least one instruction to launch an application on the device.