Abstract: The cable lasher comprises an autonomous carriage for moving along cables to be attached to each other, a low mass lashing apparatus rotatably mounted to the carriage and at least two lashing wire dispensers carried by the lashing apparatus that each dispense a lashing wire about the cables in a helical winding pattern as the cable lasher moves along a longitudinal axis and the lashing apparatus rotates to attach the cables to each other. The cable lasher also comprises a channel for receiving the cables, about which the carriage and the lashing apparatus are disposed and extending along the longitudinal axis, a lashing apparatus actuator carried by the frame that rotates the lashing apparatus when the cable lasher moves along the longitudinal axis, and an onboard power source carried by said carriage for powering the advance of the carriage and the rotation of the lashing apparatus.

Abstract: The cable lasher comprises an autonomous carriage for moving along cables to be attached to each other, a lashing apparatus rotatably mounted to the carriage and capable of dispensing a lashing wire about the cables in a helical winding pattern as the cable lasher moves along a longitudinal axis and the lashing apparatus rotates to attach the cables to each other. The cable lasher also comprises a channel for receiving the cables, about which the carriage and the lashing apparatus are disposed and extending along the longitudinal axis, a lashing apparatus actuator carried by the frame that rotates the lashing apparatus when the cable lasher moves along the longitudinal axis, and an onboard power source carried by said carriage for powering the advance of the carriage and the rotation of the lashing apparatus.

Abstract: The cable lasher comprises an autonomous carriage for moving along cables to be attached to each other, a lashing apparatus rotatably mounted to the carriage and capable of dispensing a lashing wire about the cables in a helical winding pattern as the cable lasher moves along a longitudinal axis and the lashing apparatus rotates to attach the cables to each other. The cable lasher also comprises a channel for receiving the cables, about which the carriage and the lashing apparatus are disposed and extending along the longitudinal axis, a lashing apparatus actuator carried by the frame that rotates the lashing apparatus when the cable lasher moves along the longitudinal axis, and an onboard power source carried by said carriage for powering the advance of the carriage and the rotation of the lashing apparatus.

Abstract: The cable lasher comprises an autonomous carriage for moving along cables to be attached to each other, a low mass lashing apparatus rotatably mounted to the carriage and at least two lashing wire dispensers carried by the lashing apparatus that each dispense a lashing wire about the cables in a helical winding pattern as the cable lasher moves along a longitudinal axis and the lashing apparatus rotates to attach the cables to each other. The cable lasher also comprises a channel for receiving the cables, about which the carriage and the lashing apparatus are disposed and extending along the longitudinal axis, a lashing apparatus actuator carried by the frame that rotates the lashing apparatus when the cable lasher moves along the longitudinal axis, and an onboard power source carried by said carriage for powering the advance of the carriage and the rotation of the lashing apparatus.

Abstract: A cable block including a frame having an upper section and a lower section together defining an inner opening. The lower section has a main sheave rotatably connected thereto, with a section of the main sheave defining a lower portion of the inner opening. The upper section is hingedly connected to the lower section at a pivot point, with a lever extending on an opposite side of the pivot point than the remaining of the upper section. The lever is configured to extend into the inner opening and be engageable with the support cable, when the cable block is configured in the open configuration, and to drive the upper section in rotation upon a force being exerted on the lever by the support cable, to move the cable block from an open configuration to a closed configuration.

Abstract: A cable block including a frame having an upper section and a lower section together defining an inner opening. The lower section has a main sheave rotatably connected thereto, with a section of the main sheave defining a lower portion of the inner opening. The upper section is hingedly connected to the lower section at a pivot point, with a lever extending on an opposite side of the pivot point than the remaining of the upper section. The lever is configured to extend into the inner opening and be engageable with the support cable, when the cable block is configured in the open configuration, and to drive the upper section in rotation upon a force being exerted on the lever by the support cable, to move the cable block from an open configuration to a closed configuration.

Abstract: Each computer of a peer-to-peer (P2P) network performs an iterative computer-based modeling task defined by a set of training data including at least some training data that are not accessible to the other computers of the P2P network, and by a set of parameters including a shared parameter. The modeling task optimizes an objective function comparing a model parameterized by the set of parameters with the training data. Each iteration includes: performing an iterative gradient step update of parameter values stored at the computer based on the objective function; receiving parameter values of the shared parameter from other computers of the P2P network; adjusting the parameter value of the shared parameter stored at the computer by averaging the received parameter values; and sending the parameter value of the shared parameter stored at the computer to other computers of the P2P network.

Abstract: Documents of a set of documents are represented by bag-of-words (BOW) vectors. L labeled topics are provided, each labeled with a word list comprising words of a vocabulary that are representative of the labeled topic and possibly a list of relevant documents. Probabilistic classification of the documents generates for each labeled topic a document vector whose elements store scores of the documents for the labeled topic and a word vector whose elements store scores of the words of the vocabulary for the labeled topic. Non-negative matrix factorization (NMF) is performed to generate a document-topic model that clusters the documents into k topics where k>L. NMF factors representing L topics of the k topics are initialized to the document and word vectors for the L labeled topics. In some embodiments the NMF factors representing the L topics initialized to the document and word vectors are frozen, that is, are not updated by the NMF after the initialization.

Abstract: A collective matrix is constructed, having a diagnostic sessions dimension and a diagnostic state descriptors dimension. The diagnostic state descriptors dimension includes a plurality of symptom fields, a plurality of root cause fields, and a plurality of solution fields. Collective matrix factorization of the collective matrix is performed to generate a factored collective matrix comprising a sessions factor matrix embedding diagnostic sessions and a descriptors factor matrix embedding diagnostic state descriptors. An in-progress diagnostic session is embedded in the factored collective matrix. A symptom or solution is recommended for evaluation in the in-progress diagnostic session based on the embedding. The diagnostic state descriptors dimension may further include at least one information field storing a representation (for example, a bag-of-words representation) of a semantic description of a problem being diagnosed by the in-progress diagnostic session.

Abstract: In rejection sampling of a function or distribution p over a space X, a proposal distribution q(n) is refined responsive to rejection of a sample x*?X to generate a refined proposal distribution q(n+1) selected to satisfy the criteria p(x)?q(n+1)(x)?q(n)(x) and q(n+1)(x*)<q(n)(x*). In a sampling mode, the sample x* is obtained by random sampling of the space X, the rejection sampling accepts or rejects x* based on comparison of a ratio p(x*)/q(x*) with a random draw, and the refined proposal distribution q(n+1) is selected to minimize a norm ?q(n+1)?? where ?<?. In an optimization mode, the sample x* is obtained such that q*=q(n)(x*) maximizes q(n) over the space X, the rejection sampling accepts or rejects x* based on a difference between or ratio of q* and p(x*), and the refined proposal distribution q(n+1) is selected to minimize a norm ?q(n+1)??=max{q(n+1)(x)}.

Abstract: Object clustering techniques are disclosed. A nonnegative sparse similarity matrix is constructed for a set of objects. Nonnegative factorization of the nonnegative sparse similarity matrix is performed. Objects of the set of objects are allocated to clusters based on factor matrices generated by the nonnegative factorization of the nonnegative sparse similarity matrix.

Abstract: In multi-view learning, optimized prediction matrices are determined for V?2 views of n objects, and a prediction of a view of an object is generated based on the optimized prediction matrix for that view. An objective is optimized, wherein is a set of parameters including at least the V prediction matrices and a concatenated matrix comprising a concatenation of the prediction matrices, and comprises a sum including at least a loss function for each view, a trace norm of the prediction matrix for each view, and a trace norm of the concatenated matrix. may further include a sparse matrix for each view, with further including an element-wise norm of the sparse matrix for each view. may further include regularization parameters scaling the trace norms of the prediction matrices and the trace norm of the concatenated matrix.

Abstract: Each computer of a peer-to-peer (P2P) network performs an iterative computer-based modeling task defined by a set of training data including at least some training data that are not accessible to the other computers of the P2P network, and by a set of parameters including a shared parameter. The modeling task optimizes an objective function comparing a model parameterized by the set of parameters with the training data. Each iteration includes: performing an iterative gradient step update of parameter values stored at the computer based on the objective function; receiving parameter values of the shared parameter from other computers of the P2P network; adjusting the parameter value of the shared parameter stored at the computer by averaging the received parameter values; and sending the parameter value of the shared parameter stored at the computer to other computers of the P2P network.

Abstract: In a parallel computing method performed by a parallel computing system comprising a plurality of central processing units (CPUs), a main process executes. Tasks are executed in parallel with the main process on CPUs not used in executing the main process. Results of completed tasks are stored in a cache, from which the main process retrieves completed task results when needed. The initiation of task execution is controlled by a priority ranking of tasks based on at least probabilities that task results will be needed by the main process and time limits for executing the tasks. The priority ranking of tasks is from the vantage point of a current execution point in the main process and is updated as the main process executes. An executing task may be pre-empted by a task having higher priority if no idle CPU is available.

Abstract: Documents of a set of documents are represented by bag-of-words (BOW) vectors. L labeled topics are provided, each labeled with a word list comprising words of a vocabulary that are representative of the labeled topic and possibly a list of relevant documents. Probabilistic classification of the documents generates for each labeled topic a document vector whose elements store scores of the documents for the labeled topic and a word vector whose elements store scores of the words of the vocabulary for the labeled topic. Non-negative matrix factorization (NMF) is performed to generate a document-topic model that clusters the documents into k topics where k>L. NMF factors representing L topics of the k topics are initialized to the document and word vectors for the L labeled topics. In some embodiments the NMF factors representing the L topics initialized to the document and word vectors are frozen, that is, are not updated by the NMF after the initialization.

Abstract: A collective matrix is constructed, having a diagnostic sessions dimension and a diagnostic state descriptors dimension. The diagnostic state descriptors dimension includes a plurality of symptom fields, a plurality of root cause fields, and a plurality of solution fields. Collective matrix factorization of the collective matrix is performed to generate a factored collective matrix comprising a sessions factor matrix embedding diagnostic sessions and a descriptors factor matrix embedding diagnostic state descriptors. An in-progress diagnostic session is embedded in the factored collective matrix. A symptom or solution is recommended for evaluation in the in-progress diagnostic session based on the embedding. The diagnostic state descriptors dimension may further include at least one information field storing a representation (for example, a bag-of-words representation) of a semantic description of a problem being diagnosed by the in-progress diagnostic session.

Abstract: In a parallel computing method performed by a parallel computing system comprising a plurality of central processing units (CPUs), a main process executes. Tasks are executed in parallel with the main process on CPUs not used in executing the main process. Results of completed tasks are stored in a cache, from which the main process retrieves completed task results when needed. The initiation of task execution is controlled by a priority ranking of tasks based on at least probabilities that task results will be needed by the main process and time limits for executing the tasks. The priority ranking of tasks is from the vantage point of a current execution point in the main process and is updated as the main process executes. An executing task may be pre-empted by a task having higher priority if no idle CPU is available.

Abstract: Methods and systems are presented for performing one or more printer device management functions in a network, in which affinities between printers are determined from job tracking data to indicate associations between printer devices and user devices, and the affinity data is used to perform one or more printer management functions such as determining printer connections for new or roaming user devices, print job redirection, and identification of underutilized printer device assets.

Abstract: In multi-view learning, optimized prediction matrices are determined for V?2 views of n objects, and a prediction of a view of an object is generated based on the optimized prediction matrix for that view. An objective is optimized, wherein is a set of parameters including at least the V prediction matrices and a concatenated matrix comprising a concatenation of the prediction matrices, and comprises a sum including at least a loss function for each view, a trace norm of the prediction matrix for each view, and a trace norm of the concatenated matrix. may further include a sparse matrix for each view, with further including an element-wise norm of the sparse matrix for each view. may further include regularization parameters scaling the trace norms of the prediction matrices and the trace norm of the concatenated matrix.

Abstract: In factorization of binary matrices or tensors, training algorithms usually scale linearly with the number of training examples. For very unbalanced learning problems, the number of non-zero training examples can be much smaller than the number of zeros in the full dataset. For some problems where the squared norm can be efficiently computed, the training time complexity can be reduced. A method herein receives a binary tensor defined by matrices comprising elements in a database. A processing device determines an upper bound for non-quadratic losses associated with factorization of the binary tensor. The upper bound is based on a variation parameter. The processing device performs factorization of the binary tensor by alternately minimizing the upper bound with respect to the variation parameter and minimizing the upper bound with respect to the elements of the matrices using a gradient descent method.