Abstract: One embodiment of the subject matter can facilitate forecasting by non-linearly combining prior information and leveraging prior information at any time point based on dynamic programming and a probabilistic model that considers both neighbor states and values. This embodiment has several advantages. First, the probabilistic model can be learned from training data. Second, its non-linearity facilitates improved forecasting accuracy. Third, it is efficient for prediction and can be parallelized over the training data to yield a learning time that is linear in the maximum number of elements in the sequences in the training data. Fourth, it is optimal in that it guarantees a forecast that is a most likely one based on the principle of optimality in dynamic programming and basic probability.

Abstract: One embodiment of the subject matter can facilitate three-dimensional protein structure prediction from an amino acid sequence based on dynamic programming and a probabilistic model learned from training examples. This embodiment is efficient, accurate, can easily be parallelized, and guarantees a prediction that is a most likely three-dimensional protein structure from the amino acid sequence. Moreover, this embodiment is rotation invariant and not require physical or biological knowledge to determine a protein's three-dimensional configuration based on a corresponding amino acid sequence.

Abstract: One embodiment of the subject matter can facilitate classifying a sequence based on dynamic programming and a probabilistic model that considers both neighbor states and values. This embodiment has several advantages. First, the probabilistic model can be learned from training data. Second, it is more accurate than previous models. Third, it is more efficient that previous methods for both prediction and learning. Embodiments of the subject matter can also be parallelized over the training data to yield a learning time that is linear in the maximum number of elements in the sequences in the training data. Fourth, it is optimal in that it guarantees a prediction that is a most likely one based on the probabilistic model. This guarantee is based on the principle of optimality in dynamic programming and basic probability. Fifth, it leverages locality to improve accuracy rather than throwing away or aggregating information as in feature-based methods.

Abstract: A system and method are provided for routing content requests. On a given server network, content requests comprising a character string may be routed up a hierarchical network topology until a linear chain, corresponding to the character string, is identified. Thus, the content request is forwarded up the hierarchy until an intersecting server network is reached. Then the content request is forwarded down the hierarchy until, along a published linear chain corresponding to the character string, until a content source is reached. Content is provided to the requestor along a reverse path of the content request.

Abstract: A method and system for predicting the geographic location of a network entity are described. Examples include predicting the geographic location of a network entity by directing the network entity to transmit one or more data packets to a number of predetermined network identifiers, such as IP addresses, where data corresponding to each of the network identifiers is part of a geographic location prediction model. In examples, a dataset that represents transit times for the data packets transmitted from the network entity to the hosts identified by the IP addresses is determined, and a geographic location for the network entity is predicted by applying the geographic location prediction model to the dataset.

Abstract: A method and system for predicting the geographic location of a network entity are described. Examples include predicting the geographic location of a network entity by directing the network entity to transmit one or more data packets to a number of predetermined network identifiers, such as IP addresses, where data corresponding to each of the network identifiers is part of a geographic location prediction model. In examples, a dataset that represents transit times for the data packets transmitted from the network entity to the hosts identified by the IP addresses is determined, and a geographic location for the network entity is predicted by applying the geographic location prediction model to the dataset.

Abstract: A system and method are provided for routing content requests. On a given server network, content requests comprising a character string may be routed up a hierarchical network topology until a linear chain, corresponding to the character string, is identified. Thus, the content request is forwarded up the hierarchy until an intersecting server network is reached. Then the content request is forwarded down the hierarchy until, along a published linear chain corresponding to the character string, until a content source is reached. Content is provided to the requestor along a reverse path of the content request.

Abstract: During operation, embodiments of the subject matter can perform sequence to sequence translation. Inputs can comprise a sequence of elements in one language and outputs can comprise a sequence of elements in another language, where the number of elements in the input sequence might not match the number of elements in the output sequence. Unlike in encoder-decoder approaches to sequence-to-sequence transformations, embodiments of the subject matter can use Dynamic Programming to facilitate efficient sequence to sequence translation. Unlike in Deep Learning, embodiments of the subject matter cannot be fooled by spurious correlations because they do not require an unsupervised learning step.

Abstract: A system and method are provided for routing content requests. On a given server network, content requests comprising a character string may be routed up a hierarchical network topology until a linear chain, corresponding to the character string, is identified. Thus, the content request is forwarded up the hierarchy until an intersecting server network is reached. Then the content request is forwarded down the hierarchy until, along a published linear chain corresponding to the character string, until a content source is reached. Content is provided to the requestor along a reverse path of the content request.

Abstract: A system and method are provided for routing content requests. On a given server network, content requests comprising a character string may be routed up a hierarchical network topology until a linear chain, corresponding to the character string, is identified. Thus, the content request is forwarded up the hierarchy until an intersecting server network is reached. Then the content request is forwarded down the hierarchy until, along a published linear chain corresponding to the character string, until a content source is reached. Content is provided to the requestor along a reverse path of the content request.

Abstract: One embodiment of the subject matter facilitates visualizing data by clustering a plurality of rows (i.e. the data), determining a distance between each row and each cluster, assigning the distance between each row and each cluster to a respective visual variable value (e.g. location, color, intensity, and time), and displaying the resulting visual variables in a visualization.

Abstract: One embodiment of the subject matter combines categorical and numerical variables in machine learning based on a difference table for categorical variables. During operation, the system performs the following steps. First, the system receives an input value of a categorical variable. Next, the system determines a prediction based on the input value of the categorical variable, a most likely value of the categorical variable, and a difference table for the categorical variable, where the most likely value of the categorical variable is based on a plurality of values of the categorical variable and where the difference table for the categorical variable comprises a number for each pair of values of the categorical variable. Subsequently, the system produces a result that indicates the prediction.

Abstract: A system and method are provided for routing content requests. On a given server network, content requests comprising a character string may be routed up a hierarchical network topology until a linear chain, corresponding to the character string, is identified. Thus, the content request is forwarded up the hierarchy until an intersecting server network is reached. Then the content request is forwarded down the hierarchy until, along a published linear chain corresponding to the character string, until a content source is reached. Content is provided to the requestor along a reverse path of the content request.

Abstract: During operation, embodiments of the subject matter can perform compression of an n-dimensional m-channel patch based on unsupervised learning (clustering). Embodiments of the subject matter can perform multiple such compressions of patches tessellated (tiled) across a space. Embodiments of the subject matter can also perform hierarchical compression through recursive application of embodiments of the subject matter. Embodiments of the subject matter can compress but are not limited to compressing the following: a database, a sequence, an image, a video, and a volumetric video.

Abstract: A method and system for predicting the geographic location of a network entity are described. Examples include predicting the geographic location of a network entity by directing the network entity to transmit one or more data packets to a number of predetermined network identifiers, such as IP addresses, where data corresponding to each of the network identifiers is part of a geographic location prediction model. In examples, a dataset that represents transit times for the data packets transmitted from the network entity to the hosts identified by the IP addresses is determined, and a geographic location for the network entity is predicted by applying the geographic location prediction model to the dataset.

Abstract: A system and method are provided for routing content requests. On a given server network, content requests comprising a character string may be routed up a hierarchical network topology until a linear chain, corresponding to the character string, is identified. Thus, the content request is forwarded up the hierarchy until an intersecting server network is reached. Then the content request is forwarded down the hierarchy until, along a published linear chain corresponding to the character string, until a content source is reached. Content is provided to the requestor along a reverse path of the content request.

Abstract: Embodiments of the present invention facilitate parallel distributed computing. During operation, embodiments of the present invention receive from a requesting node an operator o1 and unevaluated expressions representing arguments for that operator. Next, embodiments of the invention evaluate the arguments and then determine another unevaluated expression based on that operator and the evaluated arguments. Subsequently, embodiments of the invention send the another evaluated expression to other nodes for evaluation and receive the resulting evaluated expression, which is then returned to the requesting node.

Abstract: A method and system for predicting the geographic location of a network entity are described. Examples include predicting the geographic location of a network entity by directing the network entity to transmit one or more data packets to a number of predetermined network identifiers, such as IP addresses, where data corresponding to each of the network identifiers is part of a geographic location prediction model. In examples, a dataset that represents transit times for the data packets transmitted from the network entity to the hosts identified by the IP addresses is determined, and a geographic location for the network entity is predicted by applying the geographic location prediction model to the dataset.

Abstract: A system and method are provided for routing content requests. On a given server network, content requests comprising a character string may be routed up a hierarchical network topology until a linear chain, corresponding to the character string, is identified. Thus, the content request is forwarded up the hierarchy until an intersecting server network is reached. Then the content request is forwarded down the hierarchy until, along a published linear chain corresponding to the character string, until a content source is reached. Content is provided to the requestor along a reverse path of the content request.

Abstract: A model is implemented that includes one or more classes. For each of the one or more classes of the model, a set of input variables of the big data set are represented as a matrix with non-zero values only provided as diagonal entries. A most likely class for each input variable is determined based at least in part on inverting the matrix. One or more predictions are determined for one or more output variables based at least in part on the most likely class of one or more input variables from the set of input variables.