Abstract: The invention provides three novel disarmed strains of Agrobacterium tumefaciens bacteria useful for the transformation of plants. The invention provides three engineered A. tumefaciens Chry5 strains or bacterial cells thereof which comprise the Chry5 strain chromosomal background and a disarmed pTiChry5 vector, and methods of using said bacterial strains or cells for transformation of fungal or plant cells, in particular dicot or monocot plant cells, including soybean, maize, wheat, and sugarcane cells. The invention further relates to the transgenic plants created by these methods.

Abstract: In one embodiment, a cloud-based service instructs one or more networking devices in a local area network (LAN) to form a virtual network overlay in the LAN that redirects traffic associated with a particular node in the LAN to the service. The service receives multicast or broadcast traffic sent by the particular node in the LAN and redirected to the service via the virtual network overlay. The service identifies a group of nodes in the network that are to receive the traffic sent by the particular node, based in part by profiling the traffic associated with the particular node. The service sends the traffic sent by the particular node to at least one networking device in the LAN with an indication of the identified group of nodes in the network that are to receive the traffic sent by the particular node. The at least one networking device forwards the traffic sent by the particular node to the nodes in the identified group.

Abstract: In one embodiment, a server instructs one or more networking devices in a local area network (LAN) to form a virtual network overlay in the LAN that redirects traffic associated with a particular node in the LAN to the server. The server receives the redirected traffic associated with the particular node. The server trains a machine learning-based behavioral model for the particular node based on the redirected traffic. The server controls whether a particular redirected traffic flow associated with the node in the LAN is sent to a destination of the traffic flow using the trained behavioral model.

Abstract: In one embodiment, a device in a network identifies a node in the network that is not synchronized to a network time synchronization mechanism. The device determines a scheduled reception time for a particular deterministic traffic flow at which the device is to receive the traffic flow from the node. The device sends, prior to the scheduled reception time, a request to the node for the particular deterministic traffic flow. The request identifies the particular deterministic traffic flow and causes the node to send the traffic flow to the device. The device receives the particular deterministic traffic flow from the node at the scheduled reception time.

Abstract: A particular fat tree network node stores default routing information indicating that the particular fat tree network node can reach a plurality of parent fat tree network nodes of the particular fat tree network node. The particular fat tree network node obtains, from a first parent fat tree network node of the plurality of parent fat tree network nodes, a negative disaggregation advertisement indicating that the first parent fat tree network node cannot reach a specific destination. The particular fat tree network node determines whether the first parent fat tree network node is the only parent fat tree network node of the plurality of parent fat tree network nodes that cannot reach the specific destination. If so, the particular fat tree network node installs supplemental routing information indicating that every parent fat tree network node except the first parent fat tree network node can reach the specific destination.

Abstract: In one embodiment, a device receives data indicative of packet arrival times at a plurality of nodes along a path in a deterministic network. The device compares the packet arrival times to their corresponding scheduled delivery intervals in a deterministic communication schedule used by the nodes along the path. The device detects, using a machine learning-based anomaly detector, a time synchronization anomaly based on the comparisons between the packet arrival times and their scheduled delivery intervals. The device causes performance of a mitigation action in the network based on the detected time synchronization anomaly.

Abstract: In one embodiment, a multicast listener device floods a path lookup request to search for a multicast tree, and may then receive path lookup responses from candidate nodes on the multicast tree, where each of the path lookup responses indicates a unicast routing cost from a respective candidate node to the multicast listener device, and where each of the candidate nodes is configured to suppress a path lookup response if a total path latency from a source of the multicast tree to the multicast listener device via that respective candidate node is greater than a maximum allowable path latency. The multicast listener device may then select a particular candidate node as a join point for the multicast tree based on the particular node having a lowest associated unicast routing cost to the multicast listener device from among the candidate nodes, and joins the multicast tree at the selected join point.

Abstract: In one embodiment, a supervisory device in a network forms a virtual access point (VAP) for a node in the network. A set of access points (APs) in the network are mapped to the VAP as part of a VAP mapping and the node treats the APs in the VAP mapping as a single AP for purposes of communicating with the network. The supervisory device receives measurements from the APs in the VAP mapping regarding communications associated with the node. The supervisory device identifies a movement of the node based on the received measurements from the APs in the VAP mapping. The supervisory device adjusts the set of APs in the VAP mapping based on the identified movement of the node.

Abstract: In one embodiment, a device in a network receives a query walker agent configured to query information from a distributed set of devices in the network based on a query. The device executes the query walker agent to identify the query. The device updates state information of the executing query walker agent using local information from the device and based on the query. The device unloads the executing query walker agent after updating the state information. The device propagates the query walker agent with the updated state information to one or more of the distributed set of devices in the network, when the updated state information does not fully answer the query.

Abstract: Theft detection in data center networks may be provided. First, a first leaf switch may create an entry in a first distributed secure cache in response to an endpoint appearing on the first leaf switch. The entry may correspond to the endpoint and may be marked as having a tentative state. Then a request message may be sent to a plurality of leaf switches. The request message may comprise data identifying the endpoint. Next, a reply message may be received in response to the request message from a second leaf switch within the plurality of leaf switches. The tentative state may then be removed from the entry in response to the reply message indicating that the endpoint is valid.

Abstract: In one embodiment, a supervisory service receives a registration message broadcast by a first vehicle and captured by a RSU in the network of RSUs. The supervisory service registers the first vehicle by validating a signature of the registration message without registering a media access control (MAC) address of the first vehicle and without causing to send a registration response to the first vehicle. The supervisory service receives a message broadcast by a second vehicle addressed to the first vehicle and captured by at least one RSU in the network of RSUs. The supervisory service selects one or more RSUs in the network of RSUs to re-broadcast the message. The supervisory service controls the one or more RSUs to re-broadcast the message.

Abstract: In one embodiment, a server instructs one or more networking devices in a local area network (LAN) to form a virtual network overlay in the LAN that redirects traffic associated with a particular node in the LAN to the server. The server receives the redirected traffic associated with the particular node. The server trains a machine learning-based behavioral model for the particular node based on the redirected traffic. The server controls whether a particular redirected traffic flow associated with the node in the LAN is sent to a destination of the traffic flow using the trained behavioral model.

Abstract: A particular fat tree network node stores default routing information indicating that the particular fat tree network node can reach a plurality of parent fat tree network nodes of the particular fat tree network node. The particular fat tree network node obtains, from a first parent fat tree network node of the plurality of parent fat tree network nodes, a negative disaggregation advertisement indicating that the first parent fat tree network node cannot reach a specific destination. The particular fat tree network node determines whether the first parent fat tree network node is the only parent fat tree network node of the plurality of parent fat tree network nodes that cannot reach the specific destination. If so, the particular fat tree network node installs supplemental routing information indicating that every parent fat tree network node except the first parent fat tree network node can reach the specific destination.

Abstract: Various embodiments disclosed herein enable performing energy detection on a subset of a channel. In various embodiments, a method of performing energy detection is performed by a computing device. In various embodiments, the computing device includes a wireless transceiver, one or more processors, and a non-transitory memory. In various embodiments, the method includes performing energy detection on one or more overlapping portions of a first channel and a second channel. In some embodiments, the method includes determining whether a detected energy level from the energy detection satisfies a threshold. In some embodiments, the method includes transmitting a signal into the first channel based on the threshold being satisfied.

Abstract: In one embodiment, a networking device in a local area network (LAN) receives an instruction from a server to form a virtual network overlay in the LAN that redirects traffic associated with a particular node in the LAN to the server for analysis. The networking device establishes the virtual network overlay in the LAN to redirect traffic associated with the particular node to the server. The networking device determines that at least a portion of the traffic associated with the particular node should be processed locally within the LAN and not via redirection to the server and adjusts the virtual network overlay to process the at least a portion of the traffic associated with the particular node locally within the LAN and not via redirection to the server.

Abstract: In one embodiment, a device in a network receives a path computation agent configured to determine a path in the network that satisfies an objective function. The device executes the path computation agent to update state information regarding the network maintained by the path computation agent. The device selects a neighbor of the device in the network to execute the path computation agent based on the updated state information regarding the network. The device instructs the selected neighbor to execute the path computation agent with the updated state information regarding the network. The device unloads the path computation agent from the device after selecting the neighbor of the device to execute the path computation agent.

Abstract: In one embodiment, a server instructs one or more networking devices in a local area network (LAN) to form a virtual network overlay in the LAN that redirects traffic associated with a particular node in the LAN to the server. The server identifies a configuration for the particular node based on a node profile for the particular node. The server accesses a configuration interface of the particular node and instructs the particular node to use the identified configuration via the accessed configuration interface.

Abstract: In one embodiment, a supervisory device in a network receives from a plurality of access points (APs) in the network data regarding a network availability request broadcast by a node seeking to access the network and received by the APs in the plurality. The supervisory device uniquely associates the node with a virtual access point (VAP) for the node and forms a VAP mapping between the VAP for the node and a set of the APs in the plurality selected based on the received data regarding the network availability request. One of the APs in the mapping is designated as a primary access point for the node. The supervisory device instructs the primary AP to send a network availability response to the node that includes information for the VAP. The node uses the information for the VAP to access the network via the set of APs in the VAP mapping.

Abstract: In one embodiment, a device receives data indicative of packet arrival times at a plurality of nodes along a path in a deterministic network. The device compares the packet arrival times to their corresponding scheduled delivery intervals in a deterministic communication schedule used by the nodes along the path. The device detects, using a machine learning-based anomaly detector, a time synchronization anomaly based on the comparisons between the packet arrival times and their scheduled delivery intervals. The device causes performance of a mitigation action in the network based on the detected time synchronization anomaly.

Abstract: In one embodiment, a supervisory device in a network assigns different access points in the network to different access point groupings. Each of the different access point groupings uses a different network path to communicate with a given endpoint in the network. The supervisory device selects at least one of the access points in each of the different access point groupings for mapping to a virtual access point (VAP) for a node in the network as part of a VAP mapping. The supervisory device instructs the selected access points to form a VAP for the node. The node treats the access points in the VAP mapping as a single access point for purposes of communicating with the network.