Facilitating Discovery of Virtual Assistant Capabilities via Augmented Reality

Users may not be aware of all possible tasks that a virtual assistant can perform. This disclosure describes techniques for user-permitted use of augmented reality (AR) technology to help users discover contextually relevant tasks that a virtual assistant could perform. With user permission, the relevant tasks are determined by recognizing entities and/or locations within the camera view of the user’s device. Additional data from device sensors and/or other known user information such as devices owned or apps installed can also be utilized with user permission. The contextually relevant tasks are recommended to the user as an augmented message, via audio, or other interfaces, thus helping the user discover virtual assistant capabilities

Context-aware Message Delivery With Sender-Specified Priority

Solutions to manage incoming messages such as email, chat, etc. put the burden on the message recipient. In the absence of cues from the message sender about the urgency and/or importance, the recipient cannot determine whether a message requires immediate attention without reading the message first. As a result, many users hesitate to adopt solutions that defer message delivery or notification out of a fear of missing an urgent message that requires prompt action. This disclosure describes techniques to provide message senders capabilities to augment sent messages by specifying additional information, such as priority and contextual delivery parameters. Recipient applications can use the sender-provided additional information about the message to manage incoming message delivery. With user permission, relevant information regarding the recipient’s context can be used as an additional factor in determining appropriate message delivery times

Recognition of spelled out words in spoken queries

This disclosure describes techniques to enhance automated speech recognition by enabling automatic recognition of words spelled out by users. Machine learning techniques are utilized to detect explicit user intent to spell out a word as well as detect spelled out words without an explicitly stated user intent. If it is determined that the user is spelling a word, a spelling mode is triggered wherein received letters are concatenated together to form a word. If the user permits, data that includes the user context, audio of the word, audio of the user spelling out the word, and the textual representation of the word are obtained and utilized for training. The trained machine learning model is utilized in subsequent processing of user speech

Display and Voice-free Gesture-based Sleep Timer Control

Interaction with a computing device often causes the device to emit light, e.g., when the interaction causes a turned-off device screen to turn on, or audio, e.g., when the interaction causes an audible beep or other output from the device. Such light or audio emissions are undesirable in certain situations, e.g., when extending a sleep timer. This disclosure provides a voice-free gesture-based sleep timer control that enables extending a sleep timer without light or sound emissions from a device that is performing audio playback. The described techniques can also be used to detect user gestures that are mapped to specific tasks to be performed by the device

The linear hidden subset problem for the (1+1) EA with scheduled and adaptive mutation rates

We study unbiased $(1+1)$ evolutionary algorithms on linear functions with an unknown number $n$ of bits with non-zero weight. Static algorithms achieve an optimal runtime of $O(n (\ln n)^{2+\epsilon})$, however, it remained unclear whether more dynamic parameter policies could yield better runtime guarantees. We consider two setups: one where the mutation rate follows a fixed schedule, and one where it may be adapted depending on the history of the run. For the first setup, we give a schedule that achieves a runtime of $(1\pm o(1))\beta n \ln n$, where $\beta \approx 3.552$, which is an asymptotic improvement over the runtime of the static setup. Moreover, we show that no schedule admits a better runtime guarantee and that the optimal schedule is essentially unique. For the second setup, we show that the runtime can be further improved to $(1\pm o(1)) e n \ln n$, which matches the performance of algorithms that know $n$ in advance. Finally, we study the related model of initial segment uncertainty with static position-dependent mutation rates, and derive asymptotically optimal lower bounds. This answers a question by Doerr, Doerr, and K\"otzing

A Hippocampal Model for Behavioral Time Acquisition and Fast Bidirectional Replay of Spatio-Temporal Memory Sequences

The hippocampus is known to play a crucial role in the formation of long-term memory. For this, fast replays of previously experienced activities during sleep or after reward experiences are believed to be crucial. But how such replays are generated is still completely unclear. In this paper we propose a possible mechanism for this: we present a model that can store experienced trajectories on a behavioral timescale after a single run, and can subsequently bidirectionally replay such trajectories, thereby omitting any specifics of the previous behavior like speed, etc, but allowing repetitions of events, even with different subsequent events. Our solution builds on well-known concepts, one-shot learning and synfire chains, enhancing them by additional mechanisms using global inhibition and disinhibition. For replays our approach relies on dendritic spikes and cholinergic modulation, as supported by experimental data. We also hypothesize a functional role of disinhibition as a pacemaker during behavioral time

A tight Erdos-Pósa function for long cycles

ISSN:0095-895