Amazon Technologies, Inc.

Google Inc.

Rui Wang

Junlan Zhou

Trausti Thor Kristjansson

Tarun Pruthi

Krishna Kamath Koteshwara

Hassan Wassel

Shidong Zhang

Leonid Poutievski

Shaileshh Bojja Venkatakrishnan

Keqiang He

Amit Singh Chhetri

Philip Ryan Hilmes

Shiva Kumar Sundaram

Debprakash Patnaik

Harsha Inna Kedage Rao

Jianhui Wu

Yuzhou Liu

Xiaoxue Li

Weiqiang Li

Jianan Zhang

Methods are provided for mitigating hash correlation. In this regard, a hash correlation may be found between a first switch and a second switch in a network. In this network, a first egress port is to be selected among a first group of egress ports at the first switch for forwarding packets, and a second egress port is to be selected among a second group of egress ports at the second switch for forwarding packets, where the first group has a first group size and the second group has a second group size. Upon finding the hash correlation, a new second group size coprime to the first group size may be selected, and the second group of egress ports may be mapped to a mapped group having the new second group size. The second switch may be configured to route packets according to the mapped group.

Method to mitigate hash correlation in multi-path networks

Techniques for presence-detection devices to vary presence-detection sensitivity to detect different types of movements by objects, such as major movements and minor movements, using ultrasonic signals. The devices detect movement of a person in an environment by emitting ultrasonic signals into the environment, and characterizing the change in the frequency, or the Doppler shift, of the reflections of the ultrasonic signals off the person caused by the movement of the person relative to the presence-detection devices. The presence-detection devices may control the presence-detection sensitivity in order to detect major movements, such as a user walking in a room, as well as objects minor movements, such as a user typing at a computer. By adjusting the presence-detection sensitivity, the devices are able to improve the overall accuracy of detecting the presence of users in an environment.

Controlling sensitivity of presence detection using ultrasonic signals

Techniques for calibrating presence-detection devices to account for various factors that can affect the presence-detection devices' ability to detect movement. Presence-detection devices may detect movement of a person in an environment by emitting ultrasonic signals into the environment, and characterizing the change in the frequency, or the Doppler shift, of the reflections of the ultrasonic signals off the person caused by the movement of the person. However, factors such as environmental acoustic conditions, noise sources, etc., may affect the ability of the presence-detection devices to detect movement. To calibrate for these factors, the presence-detection devices may use a loudspeaker to emit an ultrasonic sweep signal that spans different frequencies in an ultrasonic frequency range. The presence-detection devices may generate audio data using a microphone that represents the ultrasonic sweep signal, and analyze that audio data to determine an optimal frequency range and/or transmission power for subsequent ultrasonic signal transmissions.

Device calibration for presence detection using ultrasonic signals

A datacenter network can be made of points of deliveries and patch panels. Rewiring the logical links within the datacenter network to meet a new network topology is computationally intense. Methods, systems, and apparatuses are provided to modify an existing network topology with a provided existing physical topology and logical topology into the new network topology. For example, the provided physical topology can include changes to the network, such as adding new points of delivery, adding additional patch panels, increasing the number of physical connections between points of delivery and patch panels, or removing a point of delivery.

Configuring data center network wiring

A system capable of detecting human presence based on output from a model-free detector and model-based detector(s). For example, the model-free detector may identify acoustic events and the model-based detectors can determine specific types of acoustic events and whether the acoustic events are associated with human activity. Using output from the model-based detectors, a device may confirm that an acoustic event identified by the model-free detector is associated with human activity or may determine that the acoustic event is associated with non-human activity and can be ignored. Thus, the device may detect human presence based on a wide variety of noises while reducing a number of false positives associated with the model-free detector.

Hybrid audio-based presence detection

An audio capture device that incorporates a beamformer and beam-specific trigger word detection. Audio data from each beam is processed by a low power trigger word detector, such as a neural network or other trained model to detect if audio data (such as an audio frame or feature vector corresponding thereto) likely includes part of a trigger word. The beam that either most strongly represents a trigger word portion or represents a trigger word portion most early in time may be selected for further processing such as speech processing or confirmation by a more robust power intensive trigger word detector.

Trigger word based beam selection

Systems and methods are disclosed for generating recommendation rules based on the attributes of items that are purchased together at a threshold rate. The attributes of the items may be extracted from item-detail content associated with the items. Using a count of the frequency with which pairs of items include pairs of attributes, a recommendation rule can be created that recommends items with particular attributes to users who access other items with particular attributes. Further, using the recommendation rules, items may be selected for recommendation to users who access an item that lacks historical access data from which to generate recommendations solving the 'cold-start' problem. Moreover, negative rules may be generated based on historical access data and attributes of items purchased and/or not purchased together at a threshold rate to prevent the recommendation of particular items to users who access items associated with the negative rules.

Recommendation generation for infrequently accessed items

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Sunnyvale, CA, United States of America

Rui Wang