Abstract: Systems described herein operate to improve network performance in a multi-tenant cloud computing environment. Systems can include communication circuitry and processing circuitry to generate a phase sequence matrix that indicates the identity and number of phases of a workload by measuring resources of the compute node during execution of the workload throughout a lifetime of the workload. The processing circuitry can generate a workload fingerprint that includes the phase sequence matrix and a phrase residency matrix. The phase residency matrix can indicate the fraction of execution time of the workload spent in each phase identified in the phase sequence matrix. A cloud controller can access the workload fingerprint for multiple workloads operating on multiple compute nodes in the cloud cluster to adjust workload allocations based at least on these workload fingerprints and on whether service level objectives (SLOs) are being met.

Abstract: Aspects of the disclosure relate to communication systems, apparatus and methods which enable or support transmitting/receiving downlink control information (DCI) carrying a downlink grant or an uplink grant. A method includes detecting a reference length based on an effective length of a downlink (DL) grant DCI and an effective length of an uplink (UL) grant DCI and calculating a length difference based on the effective DL grant DCI length and the effective UL grant DCI length. A length of a padding field in the DL grant DCI and/or the UL grant DCI is set equal to the calculated length difference such that the lengths of the DL grant DCI and UL grant DCI are both equal to the reference length. Padding bits are inserted in the padding field of the DL grant DCI and/or the UL grant DCI. Thereafter, the DL grant DCI and/or the UL grant DCI is encoded and transmitted to a scheduled entity.

Abstract: Motion capture system with a motion capture element that uses two or more sensors to measure a single physical quantity, for example to obtain both wide measurement range and high measurement precision. For example, a system may combine a low-range, high precision accelerometer having a range of ?24 g to +24 g with a high-range accelerometer having a range of ?400 g to +400 g. Data from the multiple sensors is transmitted to a computer that combines the individual sensor estimates into a single estimate for the physical quantity. Various methods may be used to combine individual estimates into a combined estimate, including for example weighting individual estimates by the inverse of the measurement variance of each sensor. Data may be extrapolated beyond the measurement range of a low-range sensor, using polynomial curves for example, and combined with data from a high-range sensor to form a combined estimate.

Abstract: Methods, systems, and devices for wireless communication are described. The methods, systems, and devices may include receiving control information from a serving station, synchronizing with an end device based at least in part on the control information received from the serving station, and receiving an acknowledgement (ACK) from the end device in response to the synchronizing, the ACK indicating the end device is synchronized with the relay device. The methods, systems, and devices may also include receiving a data packet from the end device subsequent to the synchronizing, and transmitting, to the serving station, the data packet received from the end device, the transmitting based at least in part on the control information received from the serving station.

Abstract: Wireless communications systems and methods related to scheduling communications in a network are provided. A wireless communication device communicates a semi-persistent schedule (SPS) for communicating with a group of wireless communication devices, the SPS including a plurality of resource allocations spaced apart in time. The first wireless communication device identifies a first resource allocation for at least a subset of the group of wireless communication devices based on a missed resource allocation of the plurality of resource allocations associated with a failed listen-before-talk (LBT). Additionally, the wireless communication device communicates with at least the subset of the group of wireless communication devices, communication signals based on the first resource allocation.

Abstract: The present application relates to a method of treating migraine or cluster headache in a human patient, said method comprising administering subcutaneously composition comprising sumatriptan or its pharmaceutically acceptable salt, in an amount equivalent to 3 mg sumatriptan base.

Abstract: A coordinated network service that facilitates the design and implementation of a coordinated device network of IoT devices. The coordinated network service defines modules for individual IoT devices or coordinated devices that specify the necessary inputs to the device, the outputs from the device and communication protocols. Via an interface, user devices can select a set of IoT devices and specify how they are connected and the decision making logic associated with communication flow. The coordinated network service can then automatically generate mapping information that implements the decision making logic and provides necessary transformations for communications between the specified devices. The selected modules and mappings form a workflow for the coordinated device network. The coordinated network service can then generate executable code to implement the formed workflow in a coordinated device network.

Abstract: Methods, systems, and devices for wireless communications are described that support resource utilization based event triggering. A wireless node in a wireless communications system may establish a connection with a core network via a path that includes one or more relay nodes. The wireless node may determine that a network load associated with one or more paths between the wireless node and the core network has changed, or that a difference between two or more network loads of different paths has changed. Based on such a determination, the wireless node may transmit a report to the network. In some cases, the network may receive the report from the wireless node, and may initiate a path change based on the report.

Abstract: Apparatus and methods for error modeling and correction in a blood analyte sensor or system. In one exemplary embodiment, the apparatus employs: (i) a training mode of operation, whereby the apparatus conducts “machine learning” to model one or more errors (e.g., unmodeled variable system errors) associated with the blood analyte measurement process, and (ii) generation of an operational model (based at least in part on data collected/received in the training mode), which is applied to correct or compensate for the errors during normal operation and collection of blood analyte data. This enhances device signal stability and accuracy over extended periods, thereby enabling among other things extended periods of blood analyte sensor implantation, and “personalization” of the sensor apparatus to each user receiving an implant. In one variant, the blood analyte is glucose, and the implanted sensor utilizes an oxygen-based molecular measurement principle.

Abstract: Device detection in networks with mixed mobility devices is discussed. Devices in the network first determine a discovery mode from a plurality of discovery modes, wherein the determining is based on a mobility state of the wireless device. The device identifies a set of resources associated with the determined discovery mode, wherein each of the plurality of discovery modes is provisioned with separate resources. A discovery signal is transmitted using the identified set of resources according to the discovery mode. Thus, devices with higher mobility will generally send discovery signals more often than devices that are static or semi-static.

Abstract: In one embodiment, a computer-implemented method that comprises receiving, a user request from a first system user of a plurality of system users for a communication of health data with a health information system. The health information system includes a data store that stores health data related to the plurality of system users.

Abstract: Techniques are described for machine learning-based generation of target segments is leveraged in a digital medium environment. A segment targeting system generates training data to train a machine learning model to predict strength of correlation between a set of users and a defined demographic. Further, a machine learning model is trained with visit statistics for the users to predict the likelihood that the users will visit a particular digital content platform. Those users with the highest predicted correlation with the defined demographic and the highest likelihood to visit the digital content platform can be selected and placed within a target segment, and digital content targeted to the defined demographic can be delivered to users in the target segment.

Abstract: Methods, systems, and devices for wireless communication are described that support overlapping code block groups (CBGs) for multiple codewords. A receiving device may receive multiple codewords from a transmitting device over a set of spatial layers. The receiving device may determine an association between a set of feedback bits and code blocks (CBs) of the multiple codewords. The association may be based on a CBG configuration, which may bundle one or more sets of CBs of the one or more codewords using time boundaries (e.g., symbols) or based on a uniform or proportional CB distribution. Based on whether decoding of the codewords is successful, the receiving device may transmit the set of feedback bits. The number of feedback bits may be the same regardless of whether one or two codewords are received.

Abstract: Technologies are described herein for providing instant processing of email messages. In particular, instant processing of email messages is provided for sending emails to allow for instant communication and collaboration. According to one aspect presented herein, a method is provided for instant or near instant delivery of a message across multiple organizations. The method includes receiving the message from a client. Additionally, the method sends the message via email to multiple recipients across multiple organizations.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a base station (BS) may transmit, and a user equipment (UE) may receive, a group-common downlink control information (DCI) message, wherein the group-common DCI message includes information identifying at least one communication parameter for a plurality of UEs. The BS and the UE may communicate in accordance with the at least one communication parameter based at least in part on the BS transmitting the group-common DCI message to the plurality of UEs and the UE receiving the group-common DCI message. Numerous other aspects are provided.

Abstract: A method, apparatus and computer program product predict run-time to completion of workflows executing in a shared multi-tenant distributed compute clusters. The method, apparatus and computer program product receive a MapReduce workflow. The MapReduce workflow includes one or more MapReduce jobs for execution.

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users).

Abstract: A system that analyzes data from multiple sensors, potentially of different types, that track motions of players, equipment, and projectiles such as balls. Data from different sensors is combined to generate integrated metrics for events and activities. Illustrative sensors may include inertial sensors, cameras, radars, and light gates. As an illustrative example, a video camera may track motion of a pitched baseball, and an inertial sensor may track motion of a bat; the system may use the combined data to analyze the effectiveness of the swing in hitting the pitch. The system may also use sensor data to automatically select or generate tags for an event; tags may represent for example activity types, players, performance levels, or scoring results. The system may analyze social media postings to confirm or augment event tags. Users may filter and analyze saved events based on the assigned tags.

Abstract: Methods, systems, and devices for wireless communication are described. A base station may transmit, and a UE may receive, via a configuration message, a group identifier indicating a group of UEs with which the UE is associated, the group of UEs being one of multiple groups of UEs which are each allocated resources. The base station may transmit the group downlink control information message to the group of UEs, and the UE may receive the group downlink control information message. The UE may determine that the group downlink control information message pertains to the UE by associating the group identifier with the group downlink control information message. The base station and the UE may communicate in accordance with the group downlink control information message based at least in part on the UE being associated with the group to which the group downlink control information message pertains.

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users).