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: Drink containers include a liquid container and a cap assembly removably coupled to the liquid container. Cap assemblies include a switch, a trigger and seal bar. Upon actuating the trigger the seal bar slides back to form a drink outlet opening in the cap assembly. To hold the seal bar in an open state without additional external force from the user, the switch is slid from an unlocked position to a locked position, thereby keeping the drink outlet and a vent formed in the cap assembly open in order to cool down a warm beverage in the liquid container.

Abstract: Drink containers include a liquid container and a cap assembly removably coupled to the liquid container. Cap assemblies include a switch, a trigger and seal bar. Upon actuating the trigger the seal bar slides back to form a drink outlet opening in the cap assembly. To hold the seal bar in an open state without additional external force from the user, the switch is slid from an unlocked position to a locked positon, thereby keeping the drink outlet and a vent formed in the cap assembly open in order to cool down a warm beverage in the liquid container.

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: Drink containers include a liquid container and a cap assembly removably coupled to the liquid container. Cap assemblies include a switch, a trigger and seal bar. Upon actuating the trigger the seal bar slides back to form a drink outlet opening in the cap assembly. To hold the seal bar in an open state without additional external force from the user, the switch is slid from an unlocked position to a locked positon, thereby keeping the drink outlet and a vent formed in the cap assembly open in order to cool down a warm beverage in the liquid container.