Controls on spatial and temporal variations in sand delivery to salmonid spawning riffles

Fine sediment infiltration into gravel interstices is known to be detrimental to incubating salmonid embryos. Infiltration into spawning riffles can show large spatial variations at the scale of a morphological unit and over time, with significant implications for embryo survival. Furthermore, some process-based infiltration studies, and incubation-to-emergence models assume that fines are delivered to redds via suspension rather than bedload. This process-based 12-month study examined spatial patterns of predominantly sand infiltration into gravels in an upland trout stream, using infiltration baskets. An assessment of Rouse numbers for infiltrated sand indicated that it was transported predominantly as bedload at flow peaks. Clear temporal and spatial patterns existed, with highest rates of infiltration strongly associated with higher discharges (r2 = 0.7, p < .05). Seasonal variations in the delivery of different grain sizes were also a feature, with enhanced contributions of 0.5–2 mm sediment during elevated winter flows and 0.125–0.5 mm sediment during spring and summer; the latter is potentially harmful to the later stages of embryo incubation. Clear spatial patterns were also evident across riffles, with highest rates of infiltration tending to occur in areas of lower relative roughness—the areas competent to transport sand for longer periods. Incubation-to-emergence models should take into consideration spatial patterns of fine sediment dynamics at the pool–riffle scale, to improve prediction

Characterizing and approximating eigenvalue sets of symmetric interval matrices

We consider the eigenvalue problem for the case where the input matrix is symmetric and its entries perturb in some given intervals. We present a characterization of some of the exact boundary points, which allows us to introduce an inner approximation algorithm, that in many case estimates exact bounds. To our knowledge, this is the first algorithm that is able to guaran- tee exactness. We illustrate our approach by several examples and numerical experiments

Utilising Provenance to Enhance Social Computation

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Demo: Linux Goes Apple Picking: Cross-Platform Ad hoc Communication with Apple Wireless Direct Link

Apple Wireless Direct Link (AWDL) is a proprietary and undocumented wireless ad hoc protocol that Apple introduced around 2014 and which is the base for applications such as AirDrop and AirPlay. We have reverse engineered the protocol and explain its frame format and operation in our MobiCom '18 paper "One Billion Apples' Secret Sauce: Recipe of the Apple Wireless Direct Link Ad hoc Protocol." AWDL builds on the IEEE 802.11 standard and implements election, synchronization, and channel hopping mechanisms on top of it. Furthermore, AWDL features an IPv6-based data path which enables direct communication. To validate our own work, we implement a working prototype of AWDL on Linux-based systems. Our implementation is written in C, runs in userspace, and makes use of Linux's Netlink API for interactions with the system's networking stack and the pcap library for frame injection and reception. In our demonstrator, we show how our Linux system synchronizes to an existing AWDL cluster or takes over the master role itself. Furthermore, it can receive data frames from and send them to a MacBook or iPhone via AWDL. We demonstrate the data exchange via ICMPv6 echo request and replies as well as sending and receiving data over a TCP connection.Comment: The 24th Annual International Conference on Mobile Computing and Networking (MobiCom '18

Social Media Data in Research : Provenance Challenges

The work described here was funded by a grant from the United Kingdom’s Economic and Social Research Council Social Media - Developing Understanding, Infrastructure & Engagement (ES/M001628/1).Postprin

SC-PROV: A Provenance Vocabulary for Social Computation

The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Postprin