Abstract: One embodiment of the present invention relates to a method for performing a vehicle image reconstruction by a sensing vehicle (SV) in a wireless communication system, the method comprising: receiving a plurality of stepped-frequency-continuous-wave (SFCW) from target vehicle (TV); receiving signature waveforms in a different frequency range for the plurality of SFCWs; performing synchronization by using phase-difference-of-arrival (PDoA) based on the signature waveforms; reconstructing one or more virtual images of the TV; and deriving a real image from the one of more Virtual Image.

Abstract: An online experiment system is described that generates a randomization evaluation for an online experiment, while the experiment is ongoing, indicating whether a distribution of experiment participants allocated to one or more participant groups satisfies an expected distribution. The online experiment system analyzes one of the experiment groups to obtain an observed distribution of the subset of experiment participants included in the experiment group. The online experiment system then evaluates the observed distribution relative to the expected distribution for the experiment according to a decision criteria of a population stability index test. The decision criteria is influenced by a tuning parameter that represents a ratio of experiment participants included in the observed experiment group to experiment participants included in a different experiment group.

Abstract: A server accesses a plurality of users' sessions with the web server. Each user session indicating a page flow of a corresponding user session for a plurality of web pages provided by the web server. The server generates a learning model using a neural network based on the plurality of users' sessions. The learning model is configured to predict a next user activity based on a current page flow of a current user session. The next user activity indicating one of continuing the current user session by visiting another web page provided by the web server and ending the current user session. The server dynamically adjusts a content of a web page based on the predicted next user activity.

Abstract: Provided herein is a method of performing, by a first apparatus, wireless communication. The method may include the steps of transmitting a first signal to a second apparatus through at least one antenna; receiving a second signal from the second apparatus through each of the at least one antenna; obtaining the sequence and a distance between the first apparatus and the second apparatus based on the second signal; obtaining information related to the second apparatus based on the sequence; and obtaining first location information related to the first apparatus, based on the distance and the information.

Abstract: The described technology is generally directed towards accelerating distributed principal components in the presence of noisy channels. A federated training based method is disclosed. The method can calculate a desired common subspace for edge devices under the coordination of a server. The server can be connected to the edge devices via noisy wireless channels. A broadband communication system can be used, wherein devices can transmit local gradients by linear analog modulation over sub-channels in communication rounds for over-the-air aggregation. Before each communication round, the server can detect information of a current region. Based on the region information, an online region-adaptive power control scheme can be applied to accelerate the process.

Abstract: An inventory prediction system is described that outputs a predicted inventory item not included in a user's known inventory using a cross-category directional graph that represents item categories as nodes. The inventory prediction system implements a prediction model trained using machine learning to output the predicted inventory item using the graph and at least one item from the user's known inventory. The inventory prediction system is further configured to generate a listing recommendation for the predicted inventory item. To do so, the inventory prediction system implements a logistic regression model trained using machine learning to calculate a probability that the listing recommendation should be generated using attributes of the predicted inventory item and attributes of currently trending items.

Abstract: A server accesses a plurality of users' sessions with the web server. Each user session indicating a page flow of a corresponding user session for a plurality of web pages provided by the web server. The server generates a learning model using a neural network based on the plurality of users' sessions. The learning model is configured to predict a next user activity based on a current page flow of a current user session. The next user activity indicating one of continuing the current user session by visiting another web page provided by the web server and ending the current user session. The server dynamically adjusts a content of a web page based on the predicted next user activity.

Abstract: One embodiment of the present invention relates to a method for performing a vehicle image reconstruction by a sensing vehicle (SV) in a wireless communication system, the method comprising: receiving a plurality of stepped-frequency-continuous-wave (SFCW) from target vehicle (TV); receiving signature waveforms in a different frequency range for the plurality of SFCWs; performing synchronization by using phase-difference-of-arrival (PDoA) based on the signature waveforms; reconstructing one or more virtual images of the TV; and deriving a real image form the determined one of more Virtual Image.

Abstract: A server accesses a plurality of users' sessions with the web server. Each user session indicating a page flow of a corresponding user session for a plurality of web pages provided by the web server. The server generates a learning model using a neural network based on the plurality of users' sessions. The learning model is configured to predict a next user activity based on a current page flow of a current user session. The next user activity indicating one of continuing the current user session by visiting another web page provided by the web server and ending the current user session. The server dynamically adjusts a content of a web page based on the predicted next user activity.