Nec Corporation

Beijing Didi Infinity Technology and Development Co., Ltd.

Lu Feng

Chunchen Liu

Wenjuan Wei

Teng Ge Ma

Baoli Li

Ye Wy Wang

Zheng Pan

Xing Nian

Zugang Chen

Xiaoling Zhu

Xiaozhi Qu

Jingnan Liu

Lvye Cui

Erli Wang

Adi Lin

According to the embodiments of the present disclosure, a method, apparatus, device, and storage media for information processing are provided. The method comprises extracting a plurality of keywords from an unstructured text set for a target object. The method comprises grouping at least a portion of the plurality of keywords based on semantics of the plurality of keywords. The method further comprises determining a target factor corresponding to a group of keywords based on a result of the grouping. The target factor represents an aspect of the target object. As such, new factors that affect the target object can be identified.

Method, apparatus, device and storage medium for information processing

According to embodiments of the present disclosure, a method and apparatus for information presenting are provided. The method includes presenting a first event evolutionary graph corresponding to a media content. The first event evolutionary graph includes at least a first node representing a first event, a second node representing a second event, and a first directed edge representing a first event relationship between the first event and the second event. The first event, the second event, and the first event relationship are determined from the media content. The method further includes presenting a second event evolutionary graph in association with the first event evolutionary graph. The second event evolutionary graph comprises at least a third node representing a third event. A first event factor of at least one event of the first event or the second event matches a second event factor of the third event.

Methods and device for information presenting and information processing

According to embodiments of the present disclosure, a method and device for information presenting are provided. The method includes receiving a query for an event evolutionary graph, the query indicating at least a target event to be searched. The method further includes presenting, as a result of the query, a target event evolutionary graph comprising a plurality of nodes and at least one directed edge connecting the plurality of nodes, the plurality of nodes representing the target event and one or more events associated with the target event respectively, and the at least one directed edge representing an event relationship between events represented by connected nodes respectively. The method further includes presenting, for at least one node in the plurality of nodes, time information of at least one event represented by the at least one node, respectively.

Method and device for information presenting

Embodiments of the present disclosure provide a data processing method and an electronic device, which relate to the computer field. The method includes: acquiring a time-series dataset, the time-series dataset comprising multiple time-series data items each comprising a time and multiple attribute parameters corresponding thereto; acquiring a target attribute parameter being at least one of the multiple attribute parameters; determining, based on a time-series model, at least one influence factor of the target attribute parameter, the at least one influence factor indicating at least one attribute parameter influencing the target attribute parameter and at least one time corresponding to the at least one attribute parameter; and outputting the at least one influence factor. As such, according to the embodiments of the present disclosure, for a target attribute parameter, at least one influence factor can be determined based on a time-series dataset and a time-series model. Therefore, an accurate reference can be provided for a decision on the target attribute parameter, and the decision made in this way is more accurate.

Data processing method and electronic device

Implementations of the present disclosure relate to a method, system and program product for determining a causality between a plurality of variables. A method is provided for determining a causality between a plurality of vectors, which comprises: in response to a data set of a plurality of samples associated with the plurality of variables being collected, obtaining a matrix describing the causality between the plurality of variables, each sample among the plurality of samples comprising data that corresponds to the plurality of variables; determining a fitness degree and an expert knowledge constraint which are associated with the causality based on the data set and the matrix, wherein the expert knowledge constraint comprises at least one of an edge constraint on a direct causality between two variables in the matrix and a path constraint on an indirect causality between two variables in the matrix; building a problem formula describing the causality according to the determined fitness degree and expert knowledge constraint; and solving the built problem formula so as to obtain a candidate result of the matrix. Further, there is provided a corresponding system and computer program product.

Method, system, and computer program product for determining causality

The present disclosure provides a method for pose determination. The method may include obtaining a first pose of a subject at a first time point. The method may further include retrieving one or more first features associated with a road from a database. The road may be viewable by the subject at the first pose and at the first time point. The method may further include obtaining an image captured by the subject at a second time point. The method may further include extracting one or more second features associated with the road from the image. The method may also include determining a second pose of the subject at the second time point at least based on comparing the one or more first features associated with the road with the one or more second features associated with the road.

Systems and methods for pose determination

Embodiments of the disclosure provide methods and systems for positioning a vehicle. The system may include a communication interface configured to receive a set of point cloud data with respect to a scene captured under a first lighting condition by at least one sensor. The system may further include a storage configured to store the set of point cloud data, and a processor. The processor may be configured to simulate illumination of the scene under a second lighting condition different from the first lighting condition based on the set of point cloud data, modify the set of point cloud data based on the simulated illumination of the scene under the second lighting condition, and position the vehicle under the second lighting condition based on the modified set of point cloud data.

Systems and methods for positioning vehicles under poor lighting conditions

A method, device and system for estimating causality among observed variables are provided. In response to receiving observation data of a plurality of observed variables, a causality objective function is determined, based on fitting inconsistencies when fitting is performed using the observed variables and a sparse constraint for a causal network structure. The fitting inconsistencies are adjusted based on weighting factors of the observed variables, wherein a weighting factor of an observed variable indicates a minimum underestimate value of cost required for fitting a target variable using all other observed variables than the above observed variable. Then, the causality among the plurality of observed variables is estimated by using the observations data to optimally solve the causality objective function through sparse causal reasoning under a directed acyclic graph constraint.

Method, device and system for estimating causality among observed variables

Implementations of the present disclosure relate to a method, system and program product for determining a causality between a plurality of variables. Specifically, a method is provided for determining a causality between a plurality of vectors, which comprises: in response to a data set of a plurality of samples associated with the plurality of variables being collected, obtaining a matrix describing the causality between the plurality of variables, each sample among the plurality of samples comprising data that corresponds to the plurality of variables; determining fitness associated with the causality and sparsity associated with the causality based on the data set and the matrix, wherein the sparsity comprises a constraint on each vector among a plurality of vectors in the matrix, the constraint corresponding to a variable among the plurality of variables; building a first problem formula describing the causality according to the determined fitness and sparsity; and solving the built first problem formula so as to obtain a candidate result of the matrix. Further, there is provided a corresponding system and computer program product.

Embodiments of the present disclosure relate to a method, a device and a computer-readable storage medium for data processing. The method for data processing comprises: obtaining a set of observation samples regarding a plurality of factors, one of the set of observation samples comprising respective observed values of the plurality of factors. The method further comprises: estimating, for each of the plurality of factors and based on the set of observation samples, a distribution that differences between observed values of the factor and estimated values of the factor follow. The method further comprises determining, based at least on the estimated distribution, a causal structure representing a causal relationship among the plurality of factors. Embodiments of the present disclosure further provide a device and a computer-readable storage medium for implementing the above method. The embodiments of the present disclosure can accurately and robustly discover the causal relationship among a plurality of factors without making any assumptions about the relationship between the data distribution and the factors, and affect the observed value of the target factor based on the causal relationship.

Method, device, and medium for data processing

Embodiments of the disclosure provide methods and systems for generating a color point cloud. The method may include receiving a point cloud and a plurality of images with respect to a scene captured by a plurality of sensors associated with a vehicle as the vehicle moves along a trajectory. The method may include segmenting the point cloud into a plurality of segments each associated with a start point and an end point on the trajectory of the vehicle. The method may also include associating each segment of the point cloud with one or more of the images based on the start point and the end point. The method may further include generating color point cloud by aggregating each segment of the point cloud and the one or more of the images based on calibration parameter in different distances between the segment of the point cloud and the vehicle.

Methods and systems for color point cloud generation

Embodiments of the present disclosure provide a data processing method, an electronic device and a computer-readable storage medium. The data processing method comprises: obtaining a model representing causal relations among a plurality of variables based on a set of observation data of the plurality of variables; determining, based on the obtained model, a first and a second variables having direct causal relation in the plurality of variables; determining whether the first and second variables are independent from each other; and in response to the first and second variables being independent from each other, deleting the direct causal relation between the first and second variables from the obtained model. With the data processing method of the present disclosure, pseudo-causes can be removed effectively so that causal relations among a plurality of variables can be represented more accurately.

In response to receiving observed data of mixed observed variables, a mixed causality objective function, being suitable for continuous observed variables and discrete observed variables is determined, wherein the mixed causality objective function includes a causality objective function for continuous observed variables and a causality objective function for discrete observed variables and the fitting inconsistency is adjusted based on weighted factors of the observed variables. Then, the mixed causality objective function is optimally solved by using a mixed sparse causal inference, suitable for both continuous observed variables and discrete observed variables, using the mixed observed data under a constraint of a directed acyclic graph, to estimate causality among the observed variables.

Method, apparatus and system for estimating causality among observed variables

Embodiments of the disclosure provide systems and methods for identifying a curb based on 3-D sensor data. The system includes a communication interface configured to receive the 3-D sensor data captured of a scene by a sensor equipped on the vehicle as the vehicle travels along a trajectory. The system further includes a storage configured to store the 3-D sensor data. The system also includes at least one processor. The at least one processor is configured to create a 2-D mesh representation of the 3-D sensor data. The at least one processor is further configured to segment the 2-D mesh representation into a plurality of surfaces. The at least one processor is further configured to identify boundary mesh cells connecting each pair of adjacent surfaces wherein an altitude difference between the pair of adjacent surfaces is higher than a threshold and select mesh cells that align with a direction of the trajectory as corresponding to the curb.

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Beijing, China

Lu Feng