Amazon.com

About: Amazon.com is a company organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Computer science & Service (business). The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Enhanced guidance for electronic devices having multiple tracking modes

Abstract: The location of an electronic device can be tracked and updated in order to provide a user of the device accurate directions from the user's current location to a target location, for various types of environments. Upon detecting a trigger (e.g., detecting a QR code or detecting an access point signal), an example device can switch from using a first type of positioning element (e.g., GPS) to a second type of positioning element (e.g., using accelerometers, QR codes, etc.) in determining the user's current location. By using the appropriate type of positioning element for each environment, the device may determine the user's current location more accurately. The device may provide an overlay (e.g., arrows) for displaying the directions over images captured from the user's surroundings to provide a more realistic and intuitive experience for the user.

Method and system for storing inventory holders

Abstract: A method for retrieving inventory items within an inventory system includes receiving a retrieval request that identifies an inventory item and selecting, from a plurality of inventory stations, an inventory station to fulfill an order associated with the retrieval request. The method also includes selecting an inventory holder from a plurality of inventory holders that store the inventory item and selecting, from a plurality of mobile drive units, a mobile drive unit to move the selected inventory holder to the selected inventory station.

Long term measurements of aerosol optical properties at a primary forest site in Amazonia

Abstract: . A long term experiment was conducted in a primary forest area in Amazonia, with continuous in-situ measurements of aerosol optical properties between February 2008 and April 2011, comprising, to our knowledge, the longest database ever in the Amazon Basin. Two major classes of aerosol particles, with significantly different optical properties were identified: coarse mode predominant biogenic aerosols in the wet season (January–June), naturally released by the forest metabolism, and fine mode dominated biomass burning aerosols in the dry season (July–December), transported from regional fires. Dry particle median scattering coefficients at the wavelength of 550 nm increased from 6.3 Mm−1 to 22 Mm−1, whereas absorption at 637 nm increased from 0.5 Mm−1 to 2.8 Mm−1 from wet to dry season. Most of the scattering in the dry season was attributed to the predominance of fine mode (PM2) particles (40–80% of PM10 mass), while the enhanced absorption coefficients are attributed to the presence of light absorbing aerosols from biomass burning. As both scattering and absorption increased in the dry season, the single scattering albedo (SSA) did not show a significant seasonal variability, in average 0.86 ± 0.08 at 637 nm for dry aerosols. Measured particle optical properties were used to estimate the aerosol forcing efficiency at the top of the atmosphere. Results indicate that in this primary forest site the radiative balance was dominated by the cloud cover, particularly in the wet season. Due to the high cloud fractions, the aerosol forcing efficiency absolute values were below −3.5 W m−2 in 70% of the wet season days and in 46% of the dry season days. Besides the seasonal variation, the influence of out-of-Basin aerosol sources was observed occasionally. Periods of influence of the Manaus urban plume were detected, characterized by a consistent increase on particle scattering (factor 2.5) and absorption coefficients (factor 5). Episodes of biomass burning and mineral dust particles advected from Africa were observed between January and April, characterized by enhanced concentrations of crustal elements (Al, Si, Ti, Fe) and potassium in the fine mode. During these episodes, median particle absorption coefficients increased by a factor of 2, whereas median SSA values decreased by 7%, in comparison to wet season conditions.

The 5th AI City Challenge

Abstract: The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2) assessing tasks where the level of performance is enough to cause real-world adoption. Transportation is a segment ripe for such adoption. The fifth AI City Challenge attracted 305 participating teams across 38 countries, who leveraged city-scale real traffic data and high-quality synthetic data to compete in five challenge tracks. Track 1 addressed video-based automatic vehicle counting, where the evaluation being conducted on both algorithmic effectiveness and computational efficiency. Track 2 addressed city-scale vehicle re-identification with augmented synthetic data to substantially increase the training set for the task. Track 3 addressed city-scale multi-target multi-camera vehicle tracking. Track 4 addressed traffic anomaly detection. Track 5 was a new track addressing vehicle retrieval using natural language descriptions. The evaluation system shows a general leader board of all submitted results, and a public leader board of results limited to the contest participation rules, where teams are not allowed to use external data in their work. The public leader board shows results more close to real-world situations where annotated data is limited. Results show the promise of AI in Smarter Transportation. State-of-the-art performance for some tasks shows that these technologies are ready for adoption in real-world systems.