Abstract: The disclosed computer-implemented method may include implementing factors and conversion probabilities when matching a transportation requestor to a transportation provider. Matches between transportation requestors and transportation providers that rely solely on an estimation of arrival time may not give requestors or providers the best transportation options. Lacking these optimal transportation options, requestors and providers may move to other platforms. By looking at a various transportation factors and conversion probabilities, the method may provide optimal transportation options to both requestors and providers. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may calculate individual utility metrics for each combination of potential transportation requestors and cancellations to arrive at a more accurate total expected utility for shared transportation. In one embodiment, the method may reduce computation resource requirements by calculating each cancellation probability independently. In some examples, the method may only calculate utility metrics for some fixed number and/or percentage of the most probable combinations. In some embodiments, the method may account for travel time and/or distance when calculating utility metrics. By making matching decisions for shared transportation that account for the possibility of cancellation, the method may improve the efficiency of the transportation network. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Methods and systems for analyzing, ranking, and presenting multi-modality transportation proposals are presented. In one embodiment, a method is provided that includes receiving a request for transportation and generating proposals that include routes for transportation an origin location and a destination location of the request. The proposals each include segment(s) corresponding to a portion of a route and at least one modality identifying a type of transportation for the segment(s). Proposal scores may then be calculated for each of the proposals. Proposal scores for a proposal may be calculated based on a disutility measure of the proposal that measures a predicted inconvenience for a user following the route identified by the proposal. The proposals may be ranked based on the proposal scores and the ranking may be transmitted to a mobile device for display on the mobile device.

Abstract: Systems and methods of selecting machine learning models/algorithms for a candidate dataset are disclosed. A computer system may access historical data of a set of algorithms applied to a set of benchmark datasets; select a first algorithm of the set of algorithms; apply the first algorithm to an input dataset to create a model of the input dataset; evaluate and store results of the applying; and add the first algorithm to a set of tried algorithms. The computer system may select a next algorithm of the algorithm set via submodular optimization based on the historical data and the set of tried algorithms; apply the next algorithm to the input dataset; capture a next result based on the applying; add the next result to update the set of tried algorithms; and repeat the submodular optimization. The procedure may continue until a termination condition is reached.

Abstract: A decision tree model is generated from sample data. A visualization system may automatically prune the decision tree model based on characteristics of nodes or branches in the decision tree or based on artifacts associated with model generation. For example, only nodes or questions in the decision tree receiving a largest amount of the sample data may be displayed in the decision tree. The nodes also may be displayed in a manner to more readily identify associated fields or metrics. For example, the nodes may be displayed in different colors and the colors may be associated with different node questions or answers.

Abstract: The disclosed computer-implemented method may include instant and optimized matching of transportation requesters with transportation providers by precomputing and caching evaluations of matching schemes between existing requests and available providers, where each of the matching schemes excludes one of the available providers. The possibility of matching any new request to a given provider may then be evaluated according to the cached results of the matching scheme that excluded that provider along with an evaluation of matching the new request to the provider, which may be a computationally easy problem. Thus, the new request may be matched to a provider instantly without waiting for the next iteration of solving the global matching problem. Matching requestors and providers in this way may also improve the accuracy of estimated time of arrival information provided to requestors. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Methods and systems for detecting when users deviate from a provided transportation route and for correcting the transportation route in response to such user deviations is presented. In one embodiment, a method is provided including detecting a changed condition for a transportation route between a first location and a second location. The transportation route may include multiple transportation segments. A first transportation segment designating a first modality may be identified, wherein the changed condition decreases a likelihood that vehicles associated with the first modality will be available to service the first transportation segment. In response, a second transportation segment designating a second modality different from the first modality is generated. The first transportation segment is then replaced with the second transportation segment in the transportation route.

Abstract: The disclosed computer-implemented method may include dynamically selecting transportation options to present to a transportation requestor device based on current transportation network conditions and transportation requestor device history. In some embodiments, transportation network may have many different ways of arranging a transportation requestor's trip, such as private rides, shared rides, immediate rides, and delayed rides. In some examples, the requestor's choice of transportation option may have an impact on the transportation network. In anticipation of or in response to a transportation request, the method may determine which transportation options will better benefit the transportation network and determine which transportation options to display to the requestor and/or the prominence with which the transportation products are displayed. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A bus occupancy check safety apparatus kit for confirming seats are not occupied includes a seat occupancy cover belt system comprising a rear mount configured to be coupled to a rear of a bus. A belt has a back end coupled to the rear mount and a front end coupled to a male buckle. A front mount is coupled to a front of the bus and to a female buckle that is selectively engageable with the male buckle. The belt is drawn across a plurality of seats within the bus to engage the male buckle with the female buckle. A seat occupancy window indicator comprises a suction cup, a pull nub, and a sign having a lettered side displaying a message.

Abstract: A non-transitory computer-readable medium including instructions, which when executed by one or more processors of a computing system, causes the computing system to: access a machine learning model m, an input data point P to m, P including one or more features, and a prediction m(P) of m for P; create a set of perturbed input data points Pk from P by selecting a new value for at least one feature of P for each perturbed input data point; obtain a prediction m(Pk) for each of the perturbed input data points; analyze the predictions m(Pk) for the perturbed input data points to determine which features are most influential to the prediction; and output the analysis results to a user.

Abstract: Disclosed herein are mass spectrometers having segmented electrodes and associated methods. According to an aspect, an apparatus or mass spectrometer includes an ion source configured to generate ions from a sample. The apparatus also includes a detector configured to detect a plurality of mass-to-charge ratios associated with the ions. Further, the apparatus includes segmented electrodes positioned between the ion source and the detector. The apparatus also includes a controller configured to selectively apply a voltage across the segmented electrodes for forming a predetermined electric field profile.

Abstract: A method for automatically selecting thematically representative music is disclosed. A processor is used for using a theme-related keyword to search a keyword-indexed video repository to retrieve videos associated with the theme-related keyword; analyzing the retrieved videos to select videos with music; and extracting music tracks and features from the selected videos. The method further includes selecting representative music related to the theme from the extracted music tracks using the extracted features; and storing the selected representative music in a processor accessible memory.

Abstract: A routing and wavelength assignment method for use in an optical fiber system, comprising: (i) identifying a plurality of paths between a source node and a destination node, (ii) selecting one of the plurality of identified paths, (iii) defining within the spectrum band of the selected path one or more blocks of spectral resource, in which each block comprises either: one or more unused wavelength channels, or one or more wavelength channels having the same spectral width, (iv) obtaining an entropy value of the selected path defining the spectrum fragmentation across its spectrum band, based on a logarithm of the ratio of the number of wavelength channels in each of the one or more blocks, to the total number of wavelength channels across the spectrum band, (v) iterating (ii) to (v) until the entropy value of each of the plurality of identified paths has been determined, and (vi) choosing from the plurality of identified paths a path having the lowest entropy value.

Abstract: Systems and methods of selecting machine learning models/algorithms for a candidate dataset are disclosed. A computer system may access historical data of a set of algorithms applied to a set of benchmark datasets; select a first algorithm of the set of algorithms; apply the first algorithm to an input dataset to create a model of the input dataset; evaluate and store results of the applying; and add the first algorithm to a set of tried algorithms. The computer system may select a next algorithm of the algorithm set via submodular optimization based on the historical data and the set of tried algorithms; apply the next algorithm to the input dataset; capture a next result based on the applying; add the next result to update the set of tried algorithms; and repeat the submodular optimization. The procedure may continue until a termination condition is reached.

Abstract: A system and method enables users to selectively expose and optionally monetize their data resources, for example on a web site. Data assets such as datasets and models can be exposed by the proprietor on a public gallery for use by others. Fees may be charged, for example, per new model, or per prediction using a model. Users may selectively expose public datasets or public models while keeping their raw data private.

Abstract: A routing and wavelength assignment method for use in an optical fiber network includes (i) identifying a path between each node pair in the network, (ii) identifying a block of spectral resource within the spectrum band of the identified path of a selected node pair, (iii) calculating a spectrum entropy value of the identified path of the selected node pair based on a logarithm of the ratio of the number of wavelength channels in each of the one or more blocks, to the total number of wavelength channels across the spectrum band, (iv) iterating (ii) and (iii) in respect of each of the paths between each other node pair in the network, until a spectrum entropy value of all the paths between all the node pairs has been calculated, (v) summing the spectrum entropy value of all of the paths between all of the node pairs to obtain a network spectrum entropy value in respect of a network configuration based on the paths between the node pairs, and (vi) determining from the network spectrum entropy value whether a s

Abstract: We describe a high-level computational framework especially well suited to parallel operations on large datasets. In a system in accordance with this framework, there is at least one, and generally several, instances of an architecture deployment as further described. We use the term “architecture deployment” herein to mean a cooperating group of processes together with the hardware on which the processes are executed. This is not to imply a one-to-one association of any process to particular hardware. To the contrary, as detailed below, an architecture deployment may dynamically spawn another deployment as appropriate, including provisioning needed hardware. The active architecture deployments together form a system that dynamically processes jobs requested by a user-customer, in accordance with customer's monetary budget and other criteria, in a robust and automatically scalable environment.

Abstract: A system and method enables users to selectively expose and optionally monetize their data resources, for example on a web site. Data assets such as datasets and models can be exposed by the proprietor on a public gallery for use by others. Fees may be charged, for example, per new model, or per prediction using a model. Users may selectively expose public datasets or public models while keeping their raw data private.

Abstract: Systems and processes are disclosed for advanced text analysis in the field of big data analytics and visualization: Users can now factor text into their predictive models, alongside regression, time/date and categorical information. This is ideal for building models where text content may play a prominent role (e.g., social media or customer service logs). Multiple data types, including text fields, may be combined together in datasets and models, and may be presented in various interactive visualization displays.

Abstract: We describe a high-level computational framework especially well suited to parallel operations on large datasets. In a system in accordance with this framework, there is at least one, and generally several, instances of an architecture deployment as further described. We use the term “architecture deployment” herein to mean a cooperating group of processes together with the hardware on which the processes are executed. This is not to imply a one-to-one association of any process to particular hardware. To the contrary, as detailed below, an architecture deployment may dynamically spawn another deployment as appropriate, including provisioning needed hardware. The active architecture deployments together form a system that dynamically processes jobs requested by a user-customer, in accordance with customer's monetary budget and other criteria, in a robust and automatically scalable environment.