Abstract: Deep neural networks can be visualized. For example, first values for a first layer of nodes in a neural network, second values for a second layer of nodes in the neural network, and/or third values for connections between the first layer of nodes and the second layer of nodes can be received. A quilt graph can be output that includes (i) a first set of symbols having visual characteristics representative of the first values and representing the first layer of nodes along a first axis; (ii) a second set of symbols having visual characteristics representative of the second values and representing the second layer of nodes along a second axis; and/or (iii) a matrix of blocks between the first axis and the second axis having visual characteristics representative of the third values and representing the connections between the first layer of nodes and the second layer of nodes.

Abstract: Embodiments relate generally to systems and methods for filtering unwanted wavelengths from an IR detector. In some embodiments, it may be desired to remove or reduce the wavelengths absorbed by water, to reduce the effects of water on the detection of the target gas. In some embodiments, a filter glass may be used in the IR detector, wherein the filter glass comprises one or more materials that contain hydroxyls in their molecular structure, and wherein the spectral absorption properties of the filter glass are operable to at least reduce wavelengths of light absorbed by water from the optical, thereby reducing the IR detector's cross sensitivity to water.

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: Electronic communications can be normalized using a neural network. For example, a noncanonical communication that includes multiple terms can be received. The noncanonical communication can be preprocessed by (I) generating a vector including multiple characters from a term of the multiple terms; and (II) repeating a substring of the term in the vector such that a last character of the substring is positioned in a last position in the vector. The vector can be transmitted to a neural network configured to receive the vector and generate multiple probabilities based on the vector. A normalized version of the noncanonical communication can be determined using one or more of the multiple probabilities generated by the neural network. Whether the normalized version of the noncanonical communication should be outputted can also be determined using at least one of the multiple probabilities generated by the neural network.

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: 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: 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: 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: 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: 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: The results of electronic narrative analytics can be visualized. For example, an electronic communication that includes multiple narratives can be received. Each narrative can be segmented into respective blocks of characters. Multiple sentiments associated with the respective blocks of characters can be determined. Multiple sentiment patterns can be determined based on the multiple sentiments. The multiple sentiment patterns can be categorized into multiple sentiment pattern groups. Also, multiple semantic tags associated with the multiple sentiment patterns can be determined. Further, the multiple narratives can be categorized into multiple topic sets. A graphical user interface can be displayed visually indicating at least a portion of: the multiple sentiments, the multiple sentiment pattern groups, the multiple semantic tags, or the multiple topic sets.

Abstract: Electronic communications can be normalized using a neural network. For example, a noncanonical communication that includes multiple terms can be received. The noncanonical communication can be preprocessed by (I) generating a vector including multiple characters from a term of the multiple terms; and (II) repeating a substring of the term in the vector such that a last character of the substring is positioned in a last position in the vector. The vector can be transmitted to a neural network configured to receive the vector and generate multiple probabilities based on the vector. A normalized version of the noncanonical communication can be determined using one or more of the multiple probabilities generated by the neural network. Whether the normalized version of the noncanonical communication should be outputted can also be determined using at least one of the multiple probabilities generated by the neural network.

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: 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.