Abstract: A display system for sensing a finger of a user applied to the display system includes a display panel; a sensor for sensing the finger; a sensing light source configured to emit a first light having a first wavelength W1; and a reflective polarizer disposed between the display panel and the sensor. For a substantially normally incident light, an optical transmittance of the reflective polarizer versus wavelength for a first polarization state has a band edge such that for a first wavelength range extending from a smaller wavelength L1 to a greater wavelength L2 and including W1, where 30 nm?L2?L1?50 nm and L1 is greater than and within about 20 nm of a wavelength L3 corresponding to an optical transmittance of about 50% along the band edge, the optical transmittance has an average of greater than about 75%.

Abstract: A colored glass article includes greater than or equal to 50 mol % and less than or equal to 80 mol % SiO2; greater than or equal to 7 mol % and less than or equal to 25 mol % Al2O3; greater than or equal to 1 mol % and less than or equal to 15 mol % B2O3; greater than or equal to 5 mol % and less than or equal to 20 mol % Li2O; greater than or equal to 0.5 mol % and less than or equal to 15 mol % Na2O; greater than 0 mol % and less than or equal to 1 mol % K2O; and greater than or equal to 1×10?6 mol % and less than or equal to 1 mol % Au. R2O—Al2O3 is greater than or equal to ?5 mol % and less than or equal to 7 mol %, R2O being the sum of Li2O, Na2O, and K2O.

Abstract: A curved expandable interbody device for placement between vertebrae having an upper structure, a lower structure, and a screw mechanism, wherein actuation of the screw mechanism moves the upper and lower structures between a collapsed configuration and an expanded configuration. A deployment tool couples to the curved expandable interbody device for positioning the device between adjacent vertebrae and actuating the screw mechanism, wherein the deployment tool can lock to the curved expandable interbody device and pivot relative to the curved expandable interbody device.

Abstract: Embodiments of the present disclosure include apparatuses, computer-implemented methods, and computer program products for automatic product verification and shelf product gap analysis. Some embodiments utilize a multi-imager imaging engine to capture at least two image data objects associated with at least a near field and a far field via corresponding near and far-field imagers. The far field image data object in some embodiments is processed to identify, and/or detect and decode, product information on a product label at a shelving location for future processing. The near-field image data object may be processed to identify a product set located within the environment surrounding the product label. The information identified from each image data object may be processed to identify whether one or more product mismatches, pricing mismatches, and/or product gaps are present at the shelving location, with improved likelihood of success for each task.

Abstract: Embodiments of the present disclosure include apparatuses, computer-implemented methods, and computer program products for automatic product verification and shelf product gap analysis. Some embodiments utilize a multi-imager imaging engine to capture at least two image data objects associated with at least a near field and a far field via corresponding near and far-field imagers. The far field image data object in some embodiments is processed to identify, and/or detect and decode, product information on a product label at a shelving location for future processing. The near-field image data object may be processed to identify a product set located within the environment surrounding the product label. The information identified from each image data object may be processed to identify whether one or more product mismatches, pricing mismatches, and/or product gaps are present at the shelving location, with improved likelihood of success for each task.

Abstract: A computer-implemented method for determining features of a dataset that are indicative of anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) executing a time series decomposition algorithm on a portion of the features of the data to generate a first list of features, (3) implementing a plurality of traffic dispersion graphs to generate a second list of features, and (4) implementing an autoencoder and a random forest regressor to generate a third list of features.

Abstract: A computer-implemented method for detecting anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) applying a first plurality of algorithms to determine features of the data which may contribute to anomalous behavior of the computers, and (3) applying a second plurality of algorithms to determine which computers are behaving anomalously based upon the features.

Abstract: Systems, methods, computer-readable medium, vehicles, and engines provided herein include or implement flexible engine monitoring features. In one aspect, a user-modifiable file containing selected parameters is uploaded to a vehicle having a propulsion system, such as an engine. A computing device associated with the engine generates, without modifying reporting software or any other software, a parameter reporting list that includes the selected parameters. During operation, parameter values for the selected parameters are recorded. In another aspect, a data query is transmitted to an aerial vehicle while in-flight. The data query includes a user-modifiable file that contains selected parameters for which parameter values are to be recorded. A computing device associated with the engine generates, without modifying reporting software or any other software, a parameter reporting list that includes the selected parameters.

Abstract: Embodiments of the present disclosure include apparatuses, computer-implemented methods, and computer program products for automatic product verification and shelf product gap analysis. Some embodiments utilize a multi-imager imaging engine to capture at least two image data objects associated with at least a near field and a far field via corresponding near and far-field imagers. The far field image data object in some embodiments is processed to identify, and/or detect and decode, product information on a product label at a shelving location for future processing. The near-field image data object may be processed to identify a product set located within the environment surrounding the product label. The information identified from each image data object may be processed to identify whether one or more product mismatches, pricing mismatches, and/or product gaps are present at the shelving location, with improved likelihood of success for each task.

Abstract: A system and method for monitoring a vehicle are presented. The system includes a first imaging subsystem for acquiring a plurality of load-carrying-portion images. A cargo-detection subsystem is configured for analyzing each of the plurality of load-carrying-portion images to determine whether cargo is positioned on the load-carrying portion of the vehicle. A power-detection subsystem is configured for determining when the vehicle is running. A motion-detection subsystem is configured for determining when the vehicle is in motion. An analytics subsystem is configured for calculating at least one of (i) the amount of time that the vehicle is running, (ii) the amount of time that the vehicle is running while cargo is positioned on the load-carrying portion, (iii) the amount of time the vehicle is in motion, and (iv) the amount of time the vehicle is in motion while cargo is positioned on the load-carrying portion.

Abstract: Various embodiments described herein relate to device abuse detection using machine learning. In this regard, a system compares accelerometer data of an electronic device with a plurality of defined accelerometer threshold values to identify a primary abuse event category associated with the electronic device. In response to the primary abuse event category being identified, the system generates a first prediction for a secondary abuse event category associated with the electronic device based on a machine learning technique associated with inertial data of the electronic device, image data generated by the electronic device, and audio data captured by the electronic device. Furthermore, the system transmits the inertial data, the image data and the audio data to a network server device associated with a machine learning service to facilitate generation of a second prediction for the secondary abuse event category based on the inertial data, the image data, and the audio data.

Abstract: The disclosure embraces a system and method for providing a continuous communication link between a server computer and a symbol reading device, and a novel symbol reading device adapted for use in such a system. The system greatly simplifies the remote management, diagnostic evaluation, and monitoring of symbol reading devices, especially those devices connected to host devices that are not PC-based or running a complete operating system.

Abstract: A computer-implemented method for determining features of a dataset that are indicative of anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) executing a time series decomposition algorithm on a portion of the features of the data to generate a first list of features, (3) implementing a plurality of traffic dispersion graphs to generate a second list of features, and (4) implementing an autoencoder and a random forest regressor to generate a third list of features.

Abstract: An apparatus for use in decoding a bar code symbol may include an image sensor integrated circuit having a plurality of pixels, timing, and control circuitry for controlling an image sensor, gain circuitry for controlling gain, and analog to digital conversion circuitry for conversion of an analog signal to a digital signal. The apparatus may also include a PCB for mounting the image sensor integrated circuit and light source bank. The connection between the image sensor integrated circuit and/or light source bank and the PCB characterized by a plurality of wires connecting a plurality of bond pads and a plurality of contact pads, where the wires, bond pads, and contact pads provide electrical input/output and mechanical connections between the image sensor integrated circuit and the PCB. The apparatus may be operative for processing image signals generated by the image sensor integrated circuit for attempting to decode the bar code symbol.