Abstract: Training data for training a neural network usable for electronic sentiment analysis can be automatically constructed. For example, an electronic communication usable for training the neural network and including multiple characters can be received. A sentiment dictionary including multiple expressions mapped to multiple sentiment values representing different sentiments can be received. Each expression in the sentiment dictionary can be mapped to a corresponding sentiment value. An overall sentiment for the electronic communication can be determined using the sentiment dictionary. Training data usable for training the neural network can be automatically constructed based on the overall sentiment of the electronic communication. The neural network can be trained using the training data. A second electronic communication including an unknown sentiment can be received. At least one sentiment associated with the second electronic communication can be determined using the neural network.

Abstract: The results of electronic sentiment analysis can be visualized. For example, multiple sentiments expressed in an electronic communication can be determined using a neural network. Each sentiment of the multiple sentiments can include a positive sentiment, a neutral sentiment, or a negative sentiment. A transition between at least two sentiments of the multiple sentiments can be determined. The transition can indicate a change between the at least two sentiments occurring over a period of time. A graphical user interface visually indicating the transition between the at least two sentiments can be displayed on a timeline. The timeline can include a timeframe associated with multiple segments of the electronic communication.

Abstract: A neural network can be used to determine edit operations for normalizing an electronic communication. For example, an electronic representation of multiple characters that form a noncanonical communication can be received. It can be determined that the noncanonical communication is mapped to at least two canonical terms in a database. A recurrent neural network can be used to determine one or more edit operations usable to convert the noncanonical communication into a normalized version of the noncanonical communication. In some examples, the one or more edit operations can include inserting a character into the noncanonical communication, deleting the character from the noncanonical communication, or replacing the character with another character in the noncanonical communication. The noncanonical communication can be transformed into the normalized version of the noncanonical communication by performing the one or more edit operations.

Abstract: Electronic communications can be normalized using neural networks. For example, an electronic representation of a noncanonical communication can be received. A normalized version of the noncanonical communication can be determined using a normalizer including a neural network. The neural network can receive a single vector at an input layer of the neural network and transform an output of a hidden layer of the neural network into multiple values that sum to a total value of one. Each value of the multiple values can be a number between zero and one and represent a probability of a particular character being in a particular position in the normalized version of the noncanonical communication. The neural network can determine the normalized version of the noncanonical communication based on the multiple values. Whether the normalized version should be output can be determined based on a result from a flagger including another neural network.

Abstract: A system for machine training can comprise one or more data processors and a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including: accessing a dataset comprising data tracking a plurality of features; determining a series of values for a regularization parameter of a sparse support vector machine model, the series including an initial regularization value and a next regularization value; computing an initial solution to the sparse support vector machine model for the initial regularization value; identifying, using the initial solution, inactive features of the sparse support vector machine model for the next regularization value; and computing a next solution to the sparse support vector machine model for the next regularization value, wherein computing the next solution includes excluding the inactive features.

Abstract: A laser scanning system having a laser scanning field, and a laser beam optics module with an optical axis and including: an aperture stop disposed after a laser source for shaping the laser beam to a predetermined beam diameter; a collimating lens for collimating the laser beam produced from the aperture stop; an apodization element having a first and second optical surfaces for extending the depth of focus of the laser beam from the collimating lens; and a negative bi-prism, disposed after the apodization element, along the optical axis, to transform the energy distribution of the laser beam and cause the laser beam to converge to substantially a single beam spot along the far-field portion of the laser scanning field, and extend the depth of focus of the laser beam along the far-field portion of the laser scanning field.

Abstract: A system for machine training can comprise one or more data processors and a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including: accessing a dataset comprising data tracking a plurality of features; determining a series of values for a regularization parameter of a sparse support vector machine model, the series including an initial regularization value and a next regularization value; computing an initial solution to the sparse support vector machine model for the initial regularization value; identifying, using the initial solution, inactive features of the sparse support vector machine model for the next regularization value; and computing a next solution to the sparse support vector machine model for the next regularization value, wherein computing the next solution includes excluding the inactive features.

Abstract: A laser scanning system having a laser scanning field, and a laser beam optics module with an optical axis and including: an aperture stop disposed after a laser source for shaping the laser beam to a predetermined beam diameter; a collimating lens for collimating the laser beam produced from the aperture stop; an apodization element having a first and second optical surfaces for extending the depth of focus of the laser beam from the collimating lens; and a negative bi-prism, disposed after the apodization element, along the optical axis, to transform the energy distribution of the laser beam and cause the laser beam to converge to substantially a single beam spot along the far-field portion of the laser scanning field, and extend the depth of focus of the laser beam along the far-field portion of the laser scanning field.

Abstract: Electronic communications can be normalized using feature sets. For example, an electronic representation of a noncanonical communication can be received, and multiple candidate canonical versions of the noncanonical communication can be determined. A first feature set representative of the noncanonical communication can be determined by splitting the noncanonical communication into at least one n-gram and at least one k-skip-n-gram. Multiple comparison feature sets can be determined by splitting multiple terms in training data into respective comparison feature sets. Multiple Jaccard index values can be determined using the first feature set and the multiple comparison feature sets. A subset of the multiple terms in the training data in which an associated Jaccard index value exceeds a threshold can be selected. The subset of the multiple terms can be included in the multiple candidate canonical versions.

Abstract: An apparatus is provided. The apparatus includes a laser source and a ring-down optical resonator that performs cavity ring-down spectroscopy, the optical resonator receives coherent optical energy from the laser, wherein an extinction rate of optical resonance within the optical resonator is at least 100 times longer than an extinction rate of optical energy emitted from the laser source first following deactivation of the laser source.

Abstract: A system, method, apparatus, and computer program product for generating a cabling plan for a computing system are disclosed. A method may include accessing an elevation plan defining a respective mount position for each of a set of components of the computing system. The method may also include determining one or more port pairs to be used for interconnecting the plurality of components. The method may further include determining, for each port pair, a cable length to use to connect the port pair. The cable length may be determined based at least in part on mount positions defined by the elevation plan for the two components connected by the port pair. The method may additionally include generating a cabling plan defining the cable length to use to connect each respective port pair.

Abstract: A system, method, apparatus, and computer program product for generating an elevation plan for a computing system are disclosed. A method may include accessing a build specification identifying components to be included in the computing system. The method may also include accessing a set of component placement rules defining rules for placing components within a rack. The method may further include generating an elevation plan defining a respective mount position for each of a subset of the components within one or more racks based on the set of component placement rules.

Abstract: This disclosure provides a computer-program product, system, method and apparatus for accessing a representation of a category or item and accessing a set of multiple transactions. The transactions are processed to identify items found amongst the transactions, and the items are ordered based on an information-gain heuristic. A depth-first search for a group of best association rules is then conducted using a best-first heuristic and constraints that make the search efficient. The best rules found during the search can then be displayed to a user, along with accompanying statistics. The user can then select rules that appear to be most relevant, and further analytics can be applied to the selected rules to obtain further information about the information provided by these rules.

Abstract: A system for detecting a flame. The system may discriminate between a detected hot object and flame. The system may be a camera-like structure incorporating an infrared sensor, a lens, and an element that could filter out some of the long-wave infrared radiation. The sensor may receive radiation of a scene which forms images on the sensor. The images may be provided to a processor that incorporates one or more modules to determine whether a flame is present in the scene.

Abstract: Devices, methods, and systems for cavity-enhanced spectroscopy are described herein. One system includes an optical frequency comb (OFC) coupled to a laser source, and a cavity coupled to the OFC comprising a number of mirrors, wherein at least one of the number of mirrors is coupled to a piezo-transducer configured to alter a position of the at least one of the number of mirrors.

Abstract: Formulations of solutions and processes are described to form a substrate including a dopant. In particular implementations, the dopant can include arsenic (As) or phosphorus (P). In an embodiment, a dopant solution is provided that includes a solvent and a dopant-containing molecule. In a particular embodiment, the solvent of the dopant solution can have a flashpoint that is at least 55° C. In some cases, the dopant-containing molecule can have a molecular weight that is no greater than about 300 g/mol. In other instances, a ratio of a concentration of a dopant-containing molecule relative to a concentration of a contaminant is no greater than about 1×1010.

Abstract: Methods, processes and computer-program products are disclosed for use in a parallelized computing system in which representations of large sparse matrices are efficiently encoded and communicated between grid-computing devices. A sparse matrix can be encoded and stored as a collection of character strings wherein each character string is a Base64 encoded string representing the non-zero elements of a single row of the sparse matrix. On a per-row basis, non-zero elements can be identified by column indices and error correction metadata can be included. The resultant row data can be converted to IEEE 754 8-byte representations and then encoded into Base64 characters for storage as strings. These character strings of even very large-dimensional sparse matrices can be efficiently stored in databases or communicated to grid-computing devices.

Abstract: Systems and methods for performing analyses on data sets to display canonical rules sets with dimensional targets are disclosed. A cross-corpus rule set for a given Topic can be generated based on the entire corpus of data. A first dimensional rule set can be generated based on a first context (e.g., based on the same Topic but using a first sub-domain of the corpus of data). A second dimensional rule set can be generated based on a second context (e.g., based on the same Topic but using a second sub-domain of the corpus of data). Key dimensional differentiators (e.g., for each dimension, or context, of the Topic) can be determined based on a comparison of the general rule set, the first dimensional rule set, and the second dimensional rule set. A canonical rule set visualization can be displayed. The visualization can highlight the dimensional selectors (e.g., those tokens, or nodes, that differ between the first dimensional rule set and the second dimensional rule set).

Abstract: This disclosure provides a computer-program product, system, method and apparatus for accessing a representation of a category or item and accessing a set of multiple transactions. The transactions are processed to identify items found amongst the transactions, and the items are ordered based on an information-gain heuristic. A depth-first search for a group of best association rules is then conducted using a best-first heuristic and constraints that make the search efficient. The best rules found during the search can then be displayed to a user, along with accompanying statistics. The user can then select rules that appear to be most relevant, and further analytics can be applied to the selected rules to obtain further information about the information provided by these rules.

Abstract: This disclosure provides a computer-program product, system, method and apparatus for accessing a representation of a category or item and accessing a set of multiple transactions. The transactions are processed to identify items found amongst the transactions, and the items are ordered based on an information-gain heuristic. A depth-first search for a group of best association rules is then conducted using a best-first heuristic and constraints that make the search efficient. The best rules found during the search can then be displayed to a user, along with accompanying statistics. The user can then select rules that appear to be most relevant, and further analytics can be applied to the selected rules to obtain further information about the information provided by these rules.