Weighted homogeneous singularities and rational homology disk smoothings

We classify the resolution graphs of weighted homogeneous surface singularities which admit rational homology disk smoothings. The nonexistence of rational homology disk smoothings is shown by symplectic geometric methods, while the existence is verified via smoothings of negative weight

Bimaterial Composites via Colloidal Rolling Techniques: I, Microstructure Evolution during Rolling

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66455/1/j.1151-2916.1999.tb02259.x.pd

AUTHENTICATION AND KEY MANAGEMENT OF IOT APPLICATIONS WITH EXTENDED WIFI AUTHENTICATION (WIFI AKMA)

Internet of things (IoT) devices frequently apply insufficient authentication mechanisms with their application servers due to the constrained nature of such devices. For example, most IoT devices lack the resources that are necessary to store usernames and passwords, certificates, and keys in a secured manner. The challenge that was described above is solved in a 3rd Generation Partnership Project (3GPP) fifth-generation (5G) wireless environment through the Authentication and Key Management for Applications (AKMA) initiative. However, there is no AKMA-equivalent facility within a WiFi environment. Accordingly, techniques are presented herein that extend the WiFi authentication process to support application server authentication for constrained devices. Aspects of the presented techniques support an exchange of a WiFi key and a key identifier (which may be referred to herein as a KAKMA key and an A-KID) as part of an Extensible Authentication Protocol (EAP) tunnel using a new information element (IE) once an authentication process has successfully completed. Such an exchange allows a station (STA) device to use the key tuple {KAKMA, A-KID} to access any application functions that are grouped with that key identifier (i.e., A-KID) without requiring any further authentication

Late-Time Circumstellar Interaction in a Spitzer Selected Sample of Type IIn Supernovae

Type IIn supernovae (SNe IIn) are a rare (< 10%) subclass of core-collapse SNe that exhibit relatively narrow emission lines from a dense, pre-existing circumstellar medium (CSM). In 2009, a warm Spitzer survey observed 30 SNe IIn discovered in 2003 - 2008 and detected 10 SNe at distances out to 175 Mpc with unreported late-time infrared emission, in some cases more than 5 years post-discovery. For this single epoch of data, the warm-dust parameters suggest the presence of a radiative heating source consisting of optical/X-ray emission continuously generated by ongoing CSM interaction. Here we present multi-wavelength follow-up observations of this sample of 10 SNe IIn and the well-studied Type IIn SN 2010jl. A recent epoch of Spitzer observations reveals ongoing mid-infrared emission from nine of the SNe in this sample. We also detect three of the SNe in archival WISE data, in addition to SNe 1987A, 2004dj, and 2008iy. For at least five of the SNe in the sample, optical and/or X-ray emission confirms the presence of radiative emission from ongoing CSM interaction. The two Spitzer nondetections are consistent with the forward shock overrunning and destroying the dust shell, a result that places upper limits on the dust-shell size. The optical and infrared observations confirm the radiative heating model and constrain a number of model parameters, including progenitor mass-loss characteristics. All of the SNe in this sample experienced an outburst on the order of tens to hundreds of years prior to the SN explosion followed by periods of less intense mass loss. Although all evidence points to massive progenitors, the variation in the data highlights the diversity in SN IIn progenitor evolution. While these observations do not identify a particular progenitor system, they demonstrate that future, coordinated, multi-wavelength campaigns can constrain theoretical mass-loss models.Comment: 10 pages, 6 figures, accepted to AJ (with comments

PROACTIVE EXCHANGE OF DATA BETWEEN CLOUD PROVIDERS VIA CONTROLLER COORDINATION AND TRIGGER DYNAMIC WORKFLOWS

A multi-cloud Software Defined Network (SDN) controller proactively learns insights about subscribers, such as enterprise users, end users, and/or other cloud providers. Based on the learned insights, the multi-SDN controller applies dynamic policies on other cloud provides to which those subscribers are attached to. The multi-cloud SDN controller co-ordinates with various cloud providers, enterprise network controllers, and Internet Service Providers (ISPs) to proactively notify other cloud providers with information about affected users so that those providers can install additional resources at cloud edge/core on the fly. Additionally, the multi-cloud SDN controller facilitates a warm hand off from one cloud region to another cloud region. When the multi-cloud SDN controller learns about an enterprise outage, it proactively notifies other cloud providers of the outage event and the other cloud providers can use this for a warm hand off of session to the region(s) through which the users will be reconnected. The likely regions are derived based on telemetry obtained from multi-cloud SDN controller. The multi-cloud SDN controller also triggers a proactive cleanup of user context of the cloud provider side. The cloud provider cleans up after the connection reset event based on information from the multi-cloud SDN controller, rather than wait on a timeout of the connection

SECURITY CLASSIFICATION BASED QUIC TRAFFIC STEERING IN A SECURE INTERNET GATEWAY (SIG)

The Quick User Datagram Protocol (UDP) Internet Connection (QUIC) protocol is slated to become the next (third) major version of the Hypertext Transfer Protocol (HTTP) – i.e., HTTP/3. As applications transition to QUIC for web traffic, a Secure Internet Gateway (SIG) needs to effectively load balance, proxy, and classify QUIC traffic. Techniques are presented herein that make use of a custom Connection ID (CID) artifact to allow a load balancer to determine, with minimal processing, a target server, and potentially the application that the QUIC flow is serving, in support of steering traffic to the appropriate upstream services. Additionally, techniques are presented herein that leverage an exchange of data over an out-of-band channel in support of the enforcement of Quality of Service (QoS) requirements on an enterprise gateway