Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training neural networks through contrastive learning. In particular, the contrastive learning is modified to use a relative margin to adjust a training pair's contribution to optimization.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training neural networks through contrastive learning. In particular, the contrastive learning is modified to use a relative margin to adjust a training pair's contribution to optimization.

Abstract: The present application discloses a method and a system for analyzing and predicting a vehicle stay behavior based on multi-task learning, and the method includes the following steps: acquiring vehicle GPS and OBD data including a vehicle ID, a travel start time, a start longitude, a start latitude, an end time, an end longitude, and an end latitude after desensitization; preprocessing vehicle GPS and OBD data to obtain vehicle stay behavior data including stay location and stay duration; extract a spatial-temporal characteristic of the preprocessed vehicle stay behavior data by a deep recurrent neural network; inputting the spatial-temporal characteristic into a multi-task learning and predicting network, and obtaining the correlation between a stay location prediction task and the stay duration prediction task based on the historical stay behavior of the vehicle through the multi-task learning and predicting network to predict the stay location and stay duration.

Abstract: The present application discloses a method and a system for analyzing and predicting a vehicle stay behavior based on multi-task learning, and the method includes the following steps: acquiring vehicle GPS and OBD data including a vehicle ID, a travel start time, a start longitude, a start latitude, an end time, an end longitude, and an end latitude after desensitization; preprocessing vehicle GPS and OBD data to obtain vehicle stay behavior data including stay location and stay duration; extract a spatial-temporal characteristic of the preprocessed vehicle stay behavior data by a deep recurrent neural network; inputting the spatial-temporal characteristic into a multi-task learning and predicting network, and obtaining the correlation between a stay location prediction task and the stay duration prediction task based on the historical stay behavior of the vehicle through the multi-task learning and predicting network to predict the stay location and stay duration.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model on training data. In one aspect, one of the methods include: obtaining a training data set comprising a plurality of training inputs; obtaining data defining an original search space of a plurality of candidate data augmentation policies; generating, from the original search space, a compact search space that has one or more global hyperparameters; and training the machine learning model on the training data using one or more final data augmentation policies generated from the compact search space.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an output that characterizes a scene at a current time step. In one aspect, one of the systems include: a voxel neural network that generates a current early-stage feature representation of the current point cloud, a fusion subsystem that generates a current fused feature representation at the current time step; a backbone neural network that generates a current late-stage feature representation at the current time step, and an output neural network that generate an output that characterizes a scene at the current time step.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model on training data. In one aspect, one of the methods include: obtaining a training data set comprising a plurality of training inputs; obtaining data defining an original search space of a plurality of candidate data augmentation policies; generating, from the original search space, a compact search space that has one or more global hyperparameters; and training the machine learning model on the training data using one or more final data augmentation policies generated from the compact search space.

Abstract: Embodiments are disclosed for computing accurate smooth occluding contours. In one embodiment, a method of computing accurate smooth occluding contours includes projecting a boundary polygon associated with a first region of a three-dimensional (3D) object to a two-dimensional (2D) image plane, the boundary polygon comprising a plurality of contour vertices and edges connecting the plurality of contour vertices, triangulating the first region in the 2D image plane to generate a 2D triangulation, and generating a 3D mesh for the first region by mapping the 2D triangulation to the 3D object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing sensor data, e.g., laser sensor data, using neural networks. One of the methods includes obtaining a temporal sequence of multiple three-dimensional point clouds generated from sensor readings of an environment collected by one or more sensors within a given time period, each three-dimensional point cloud comprising a respective plurality of points in a first coordinate system; processing, using a feature extraction neural network, an input that comprises data derived from the temporal sequence of multiple three-dimensional point clouds to generate a feature embedding; receiving a query that specifies one time point within the given time period; and generating, from the feature embedding and conditioned on the query, one or more outputs that characterize one or more objects in the environment at the time point specified in the received query.

Abstract: An edge device can include a processing device, an image sensor, and a memory having instructions that are executable by the processing device for causing the processing device to perform operations. The processing device can receive, from the image sensor, an image of an environment. The processing device can determine a visibility measure corresponding to the environment by determining a dark channel of the image, determining, based on the dark channel of the image, a transmission map of the image, and determining, based on the transmission map, a visual contrast of the image. The processing device can generate information corresponding to the visibility measure.

Abstract: Disclosed is a sheep PDGFD, nucleic acids encoding PDGFD and recombinant lentivirus, host cell and use thereof, which relate to the technical field of molecular cell biology. The sheep platelet-derived growth factor PDGFD includes one or two of PDGFD-T1 and PDGFD-T2. The amino acid sequence of PDGFD-T1 is set forth in SEQ ID NO:1, and the amino acid sequence of PDGFD-T2 is set forth in SEQ ID NO:2. PDGFD-T1 and PDGFD-T2 are able to significantly inhibit the differentiation and maturation of precursor adipocytes and significantly reduce the mRNA relative expression levels of adipogenic differentiation-related genes CEBP?, PPAR?, FAS, FABP4 and LPL, thereby inhibiting animal fat deposition and improving animal meat quality, and have important guiding significance in the fields of life science, medical science, animal husbandry and the like.

Abstract: Methods, systems, and apparatus for processing point clouds using neural networks to perform a machine learning task. In one aspect, a system comprises one or more computers configured to obtain a set of point clouds captured by one or more sensors. Each point cloud includes a respective plurality of three-dimensional points. The one or more computers assign the three-dimensional points to respective voxels in a voxel grid, where the grid of voxels includes non-empty voxels to which one or more points are assigned and empty voxels to which no points are assigned. For each non-empty voxel, the one or more computers generate initial features based on the points that are assigned to the non-empty voxel. The one or more computers generate multi-scale features of the voxel grid, and the one or more computers generate an output for a point cloud processing task using the multi-scale features of the voxel grid.

Abstract: A mobile roadway sensing unit can include an image sensor that can sense image data, a network sensor that can acquire wireless signal data, and a weather sensor that can sense weather condition data. The mobile roadway sensing unit can include a processor and a memory. The memory can include instructions executable by the processor for causing the processor to acquire the sensor data including at least one of the image data, the wireless signal data, or the weather condition data from the weather sensor, wherein the sensor data represents one or more current conditions proximate the mobile roadway sensing unit. The processor can generate processed data based on the sensor data The processor can generate, based on the processed data, a notification corresponding to the one or more current conditions. The processor can provide the notification to one or more external electronic devices.

Abstract: Methods for quality control and optimizing the formation and characterization of micelles, vesicles or other aggregates are described herein. Pharmaceutically relevant peptides may be modified to form glycopeptide surfactants which form micelles or other aggregates with another surfactant. Glycopeptide and glycolipid surfactants can aggregate to form particles that enhance drug delivery. The glycopeptide surfactants may be drugs or prodrugs which are delivered via the micelles or other aggregated structures.

Abstract: Embodiments are disclosed for computing accurate smooth occluding contours. In one embodiment, a method of computing accurate smooth occluding contours includes projecting a boundary polygon associated with a first region of a three-dimensional (3D) object to a two-dimensional (2D) image plane, the boundary polygon comprising a plurality of contour vertices and edges connecting the plurality of contour vertices, triangulating the first region in the 2D image plane to generate a 2D triangulation, and generating a 3D mesh for the first region by mapping the 2D triangulation to the 3D object.

Abstract: Glycopeptide analogs of secretin family peptides, including PACAP and VIP, are described herein. These glycopeptides analogs can have neuroprotective properties and enhanced ability to cross the blood brain barrier (BBB) and/or enhanced stability. These glycosylated peptides can be used as drugs for treatment of CNS disorders, such as Parkinson's disease.

Abstract: Methods for quality control and optimizing the formation and characterization of micelles, vesicles or other aggregates are described herein. Pharmaceutically relevant peptides may be modified to form glycopeptide surfactants which form micelles or other aggregates with another surfactant. Glycopeptide and glycolipid surfactants can aggregate to form particles that enhance drug delivery. The glycopeptide surfactants may be drugs or pro-drugs which are delivered via the micelles or other aggregated structures.

Abstract: Methods for quality control and optimizing the formation and characterization of micelles, vesicles or other aggregates are described herein. Pharmaceutically relevant peptides may be modified to form glycopeptide surfactants which form micelles or other aggregates with another surfactant. Glycopeptide and glycolipid surfactants can aggregate to form particles that enhance drug delivery. The glycopeptide surfactants may be drugs or pro-drugs which are delivered via the micelles or other aggregated structures.

Abstract: A method for determining an architecture for a task neural network configured to perform a particular machine learning task is described.

Abstract: A method for determining an architecture for a task neural network configured to perform a particular machine learning task is described.