Abstract: A system is described including a telematics sensor configured to record telematics data related to a vehicle; one or more camera sensors situated within a dash-mounted camera housing installed within the vehicle, the one or more camera sensors configured to capture video frames; an atomic event identifier including one or more of a driver-facing perception ML module, a road-facing perception ML module, a telemetry ML module and a personalized driving context and history module; and an edge processor situated within the dash-mounted camera housing, the edge processor configured to: receive the video frames and the telematics data; determine whether a vehicle speed exceeds a predetermined threshold based on an output of the telematics sensor; when the vehicle speed exceeds the predetermined threshold, process the video frames and telematics data through the atomic event identifier to detect one or more atomic events; calculate a Driver Fatigue Index (DFI) score by aggregating detected atomic events with config

Abstract: The present disclosure relates to a lipid nanoparticle composition including a gallic acid derivative lipid and a use thereof, wherein it is possible to improve delivery efficiency and immune effect by changing some of the compositions of lipid nanoparticles using a bio-friendly gallic acid derivative lipid and, in particular, if a gallic acid derivative replaces a certain ratio of an ionizable lipid in a molar composition ratio, it is possible to mitigate toxicity generated by the ionizable lipid, so as to be utilized for drug delivery more safely in vivo.

Abstract: Disclosed are a multi-task training technique and resulting model for detecting distracted driving. In one embodiment, a method is disclosed comprising inputting a plurality of labeled examples into a multi-task network, the multi-task network comprising: a backbone network, the backbone network generating one or more feature vectors corresponding to each of the labeled examples, and a plurality of prediction heads coupled to the backbone network; minimizing a joint loss based on outputs of the plurality of prediction heads, the minimizing the joint loss causing a change in parameters of the backbone network; and storing a distraction classification model after minimizing the joint loss, the distraction classification model comprising the parameters of the backbone network and parameters of at least one of the prediction heads.

Abstract: Disclosed are a multi-task training technique and resulting model for detecting distracted driving. In one embodiment, a method is disclosed comprising inputting a plurality of labeled examples into a multi-task network, the multi-task network comprising: a backbone network, the backbone network generating one or more feature vectors corresponding to each of the labeled examples, and a plurality of prediction heads coupled to the backbone network; minimizing a joint loss based on outputs of the plurality of prediction heads, the minimizing the joint loss causing a change in parameters of the backbone network; and storing a distraction classification model after minimizing the joint loss, the distraction classification model comprising the parameters of the backbone network and parameters of at least one of the prediction heads.

Abstract: The present disclosure relates to a novel lipid compound and a lipid nanoparticle composition including the same and, more specifically, the lipid nanoparticle composition includes ionized lipid, helper lipid, PEG-lipid, and additives, and may mitigate changes and side effects in the delivery mechanism and enhance the protein expression efficiency by including a biofriendly vitamin-based novel lipid compound and helper lipids including neutral lipids.

Abstract: Disclosed herein are an apparatus and method for detecting a keypoint based on deep learning robust to scale changes based on information change across receptive fields. The apparatus for detecting a keypoint based on deep learning robust to scale changes based on information change across receptive fields includes a feature extractor for extracting a feature from an input image based on a pre-trained deep learning neural network, an information accumulation pyramid module for outputting, from the feature, at least two filter responses corresponding to receptive fields having different scales, an information change detection module for calculating an information change between the at least two filter responses, a keypoint detection module for creating a score map having a keypoint probability of each pixel based on the information change, and a continuous scale estimation module for estimating a scale of a receptive field having a biggest information change for each pixel.

Abstract: Disclosed are a multi-task training technique and resulting model for detecting distracted driving. In one embodiment, a method is disclosed comprising inputting a plurality of labeled examples into a multi-task network, the multi-task network comprising: a backbone network, the backbone network generating one or more feature vectors corresponding to each of the labeled examples, and a plurality of prediction heads coupled to the backbone network; minimizing a joint loss based on outputs of the plurality of prediction heads, the minimizing the joint loss causing a change in parameters of the backbone network; and storing a distraction classification model after minimizing the joint loss, the distraction classification model comprising the parameters of the backbone network and parameters of at least one of the prediction heads.

Abstract: Disclosed are a multi-task training technique and resulting model for detecting distracted driving. In one embodiment, a method is disclosed comprising inputting a plurality of labeled examples into a multi-task network, the multi-task network comprising: a backbone network, the backbone network generating one or more feature vectors corresponding to each of the labeled examples, and a plurality of prediction heads coupled to the backbone network; minimizing a joint loss based on outputs of the plurality of prediction heads, the minimizing the joint loss causing a change in parameters of the backbone network; and storing a distraction classification model after minimizing the joint loss, the distraction classification model comprising the parameters of the backbone network and parameters of at least one of the prediction heads.

Abstract: Disclosed herein is an image deep learning model training method. The method includes sampling a twin negative comprising a first negative sample and a second negative sample by selecting the first negative sample with a highest similarity out of an anchor sample and a positive sample constituting a matching pair in each class and by selecting the second negative sample with a highest similarity to the first negative sample, and training the samples to minimize a loss of a loss function in each class by utilizing the anchor sample, the positive sample, the first and second negative samples for each class. The first negative sample is selected in a different class from a class comprising the matching pair, and the second negative sample is selected in a different class from classes comprising the matching pair and the first negative sample.

Abstract: Disclosed are a multi-task training technique and resulting model for detecting distracted driving. In one embodiment, a method is disclosed comprising inputting a plurality of labeled examples into a multi-task network, the multi-task network comprising: a backbone network, the backbone network generating one or more feature vectors corresponding to each of the labeled examples, and a plurality of prediction heads coupled to the backbone network; minimizing a joint loss based on outputs of the plurality of prediction heads, the minimizing the joint loss causing a change in parameters of the backbone network; and storing a distraction classification model after minimizing the joint loss, the distraction classification model comprising the parameters of the backbone network and parameters of at least one of the prediction heads.

Abstract: Disclosed herein are an apparatus and method for detecting a keypoint based on deep learning robust to scale changes based on information change across receptive fields. The apparatus for detecting a keypoint based on deep learning robust to scale changes based on information change across receptive fields includes a feature extractor for extracting a feature from an input image based on a pre-trained deep learning neural network, an information accumulation pyramid module for outputting, from the feature, at least two filter responses corresponding to receptive fields having different scales, an information change detection module for calculating an information change between the at least two filter responses, a keypoint detection module for creating a score map having a keypoint probability of each pixel based on the information change, and a continuous scale estimation module for estimating a scale of a receptive field having a biggest information change for each pixel.

Abstract: Disclosed are a multi-task training technique and resulting model for detecting distracted driving. In one embodiment, a method is disclosed comprising inputting a plurality of labeled examples into a multi-task network, the multi-task network comprising: a backbone network, the backbone network generating one or more feature vectors corresponding to each of the labeled examples, and a plurality of prediction heads coupled to the backbone network; minimizing a joint loss based on outputs of the plurality of prediction heads, the minimizing the joint loss causing a change in parameters of the backbone network; and storing a distraction classification model after minimizing the joint loss, the distraction classification model comprising the parameters of the backbone network and parameters of at least one of the prediction heads.

Abstract: Disclosed are a multi-task training technique and resulting model for detecting distracted driving. In one embodiment, a method is disclosed comprising inputting a plurality of labeled examples into a multi-task network, the multi-task network comprising: a backbone network, the backbone network generating one or more feature vectors corresponding to each of the labeled examples, and a plurality of prediction heads coupled to the backbone network; minimizing a joint loss based on outputs of the plurality of prediction heads, the minimizing the joint loss causing a change in parameters of the backbone network; and storing a distraction classification model after minimizing the joint loss, the distraction classification model comprising the parameters of the backbone network and parameters of at least one of the prediction heads.

Abstract: A method of improving a ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCo) to have a higher protein score is disclosed. The method includes the steps of: making a modified RbcL of the RuBisCo, by, on an RbcL unit of the RuBisCo, either substituting Met for Leu, Phe, Val, or Ile or combinations thereof; substituting Lys for Arg, Thr, or His or combinations thereof; or both of these substitutions. The modified RbcL consequently modifies the RuBisCo and is added to a biomass host where it is stable for homologous recombination. Plastid and nucleus integration was observed. Example RbcL sequences are disclosed with the desirable substitutions. The improved RuBisCo can be used as an improved proteinaceous food source for humans and animals.

Abstract: An embodiment includes determining if a new incident report of a new incident matches any resolved incident reports associated with resolved incidents. The embodiment performs a first classification operation on the new incident report to determine if the new incident report is likely to be similar to any resolved incident reports associated with resolved incidents. The embodiment also performs a second classification operation on the new incident report to generate a ranked list of changes that are likely to be similar to the new incident report. The embodiment outputs the ranked list of changes to an incident manager for evaluation, then receives an input representative of a selected change from among the ranked list of changes responsible for causing the new incident. The embodiment revises the new incident report to include a reference to the selected change.

Abstract: An integrated circuit (IC) includes multiple interconnected driver cells enabled/disabled based on a first set of control signals. The multiple circuit cells are arranged to define a first aggregate enabled/disabled configuration exhibiting a first aggregated delay. The first aggregated delay is based on the individual enabled/disabled states of the circuit cells. Timing circuitry evaluates the first aggregate delay with respect to a circuit design constraint, and selectively generates a second set of control signals to configure the multiple circuit cells to define a second aggregate enabled/disabled configuration having a second aggregate delay different than the first aggregate delay.

Abstract: Disclosed herein is an image deep learning model training method. The method includes sampling a twin negative comprising a first negative sample and a second negative sample by selecting the first negative sample with a highest similarity out of an anchor sample and a positive sample constituting a matching pair in each class and by selecting the second negative sample with a highest similarity to the first negative sample, and training the samples to minimize a loss of a loss function in each class by utilizing the anchor sample, the positive sample, the first and second negative samples for each class. The first negative sample is selected in a different class from a class comprising the matching pair, and the second negative sample is selected in a different class from classes comprising the matching pair and the first negative sample.

Abstract: An integrated circuit (IC) includes multiple interconnected driver cells enabled/disabled based on a first set of control signals. The multiple circuit cells are arranged to define a first aggregate enabled/disabled configuration exhibiting a first aggregated delay. The first aggregated delay is based on the individual enabled/disabled states of the circuit cells. Timing circuitry evaluates the first aggregate delay with respect to a circuit design constraint, and selectively generates a second set of control signals to configure the multiple circuit cells to define a second aggregate enabled/disabled configuration having a second aggregate delay different than the first aggregate delay.

Abstract: A computer-implemented method for manufacturing an integrated circuit chip is disclosed. The method includes selecting cell-based circuit representations to define an initial circuit design. The initial circuit design is partitioned into multiple sub-design blocks to define a partitioned design. Circuit representations of local clock sources are inserted into the partitioned design. Each local clock source is for clocking a respective sub-design block and based on a global clock source. A timing analysis is performed to estimate skew between each local clock source and the global clock source. The partitioned design is automatically modified based on the estimated skew.

Abstract: An integrated circuit (IC) device is disclosed. The IC device includes a global clock source to generate a global clock signal. Multiple local clock sources are employed in the IC device. Each local clock source provides a local clock signal for a partitioned sub-design block in the IC device. Each local clock signal is based on the global clock signal. The IC device includes a clock controller having inputs from the global clock source and the multiple local clock sources. The clock controller (1) measures skew between each local clock source and the global clock source, and (2) generates respective control signals to adjust respective phases of each local clock signal to reduce the measured skew.