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: 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: 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.