Abstract: Convolutional neural networks can be visualized. For example, a graphical user interface (GUI) can include a matrix of symbols indicating feature-map values that represent a likelihood of a particular feature being present or absent in an input to a convolutional neural network. The GUI can also include a node-link diagram representing a feed forward neural network that forms part of the convolutional neural network. The node-link diagram can include a first row of symbols representing an input layer to the feed forward neural network, a second row of symbols representing a hidden layer of the feed forward neural network, and a third row of symbols representing an output layer of the feed forward neural network. Lines between the rows of symbols can represent connections between nodes in the input layer, the hidden layer, and the output layer of the feed forward neural network.

Abstract: A non-dispersive photoacoustic gas detector comprises an infrared light source, a closed chamber, a gas sample disposed within the closed chamber, and an acoustic sensor in fluid communication with the dosed chamber. The dosed chamber comprises at least one window that is substantially transparent to infrared light from the infrared light source, and the gas sample comprises a gas or a mixture of at least two gases.

Abstract: A non-dispersive photoacoustic gas detector includes an infrared light source, a first closed chamber, a first acoustic sensor in fluid communication with the first closed chamber, a second closed chamber, and a second acoustic sensor in fluid communication with the second closed chamber. The first closed chamber comprises a plurality of windows that are substantially transparent to infrared light from the infrared light source. The second closed chamber comprises at least one window that is substantially transparent to infrared light from the infrared light source, and the first closed chamber is arranged in series with the second closed chamber between the infrared light source and the second closed chamber.

Abstract: A system, method, apparatus, and computer program product for validating a user modification to an elevation plan for a computing system are disclosed. A method may include causing display of a user interface including a representation of at least a portion of the elevation plan. The user interface may be configured to enable user modification of the elevation plan. The method may further include receiving an indication of a mount position for a component. The method may additionally include validating that the component is mountable in the mount position. The method may also include updating the elevation plan to include the component in the mount position in response to validation that the component is mountable in the mount position.

Abstract: Interactive visualizations of a convolutional neural network are provided. For example, a graphical user interface (GUI) can include a matrix having symbols indicating feature-map values that represent likelihoods of particular features being present or absent at various locations in an input to a convolutional neural network. Each column in the matrix can have feature-map values generated by convolving the input to the convolutional neural network with a respective filter for identifying a particular feature in the input. The GUI can detect, via an input device, an interaction indicating that that the columns in the matrix are to be combined into a particular number of groups. Based on the interaction, the columns can be clustered into the particular number of groups using a clustering method. The matrix in the GUI can then be updated to visually represent each respective group of columns as a single column of symbols within the matrix.

Abstract: A method for generating a power cabling plan may include accessing a build plan. The build plan may identify a set of power drawing components and a set of PDUs. Each PDU may include at least one circuit. The method may further include iteratively assigning each of the power drawing components to at least one circuit based on an existing power load assigned to each circuit. The method may additionally include reassigning at least one power drawing component between circuits in a circuit pair if a difference in assigned power loads between circuits in any circuit pair is reducible. The method may also include generating a power cabling plan defining at least one assigned PDU port for each power drawing component. Each assigned PDU port may correspond to a circuit to which the power drawing component has been assigned.

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: Interactive visualizations of a convolutional neural network are provided. For example, a graphical user interface (GUI) can include a matrix having symbols indicating feature-map values that represent likelihoods of particular features being present or absent at various locations in an input to a convolutional neural network. Each column in the matrix can have feature-map values generated by convolving the input to the convolutional neural network with a respective filter for identifying a particular feature in the input. The GUI can detect, via an input device, an interaction indicating that that the columns in the matrix are to be combined into a particular number of groups. Based on the interaction, the columns can be clustered into the particular number of groups using a clustering method. The matrix in the GUI can then be updated to visually represent each respective group of columns as a single column of symbols within the matrix.

Abstract: Convolutional neural networks can be visualized. For example, a graphical user interface (GUI) can include a matrix of symbols indicating feature-map values that represent a likelihood of a particular feature being present or absent in an input to a convolutional neural network. The GUI can also include a node-link diagram representing a feed forward neural network that forms part of the convolutional neural network. The node-link diagram can include a first row of symbols representing an input layer to the feed forward neural network, a second row of symbols representing a hidden layer of the feed forward neural network, and a third row of symbols representing an output layer of the feed forward neural network. Lines between the rows of symbols can represent connections between nodes in the input layer, the hidden layer, and the output layer of the feed forward neural network.

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: Systems and methods are provided for use in facilitating payment account transactions, through consumer communication devices, at merchant locations. One exemplary method includes capturing a merchant indicator for a merchant in connection with a payment account transaction at the merchant for at least one product and receiving a checkout token for the payment account transaction from a person-to-merchant (P2M) engine. The checkout token is based, at least in part, on the captured merchant indicator. The method further includes transmitting a request for the payment account transaction at the merchant, where the request includes the checkout token, and whereby the consumer is permitted to purchase one or more products from the merchant, using the consumer's communication device and without interacting with a point-of-sale (POS) terminal.

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: 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: 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: 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: 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.