Existing distribution compression methods, like Kernel Herding (KH), were originally developed for unlabelled data. However, no existing approach directly compresses the conditional distribution of *labelled* data. To address this gap, we first introduce the *Average Maximum Conditional Mean Discrepancy* (AMCMD), a metric for comparing conditional distributions, and derive a closed form estimator. Next, we make a key observation: in the context of distribution compression, the cost of constructing a compressed set targeting the AMCMD can be reduced from $\mathcal{O}(n^3)$ to $\mathcal{O}(n)$. Leveraging this, we extend KH to propose *Average Conditional Kernel Herding* (ACKH), a linear-time greedy algorithm for constructing compressed sets that target the AMCMD. To better understand the advantages of *directly* compressing the conditional distribution rather than doing so via the joint distribution, we introduce *Joint Kernel Herding* (JKH), an adaptation of KH designed to compress the joint distribution of labelled data. While herding methods provide a simple and interpretable selection process, they rely on a greedy heuristic. To explore alternative optimisation strategies, we also propose *Joint Kernel Inducing Points* (JKIP) and *Average Conditional Kernel Inducing Points* (ACKIP), which *jointly* optimise the compressed set while maintaining linear complexity. Experiments show that directly preserving conditional distributions with ACKIP outperforms both joint distribution compression and the greedy selection used in ACKH. Moreover, we see that JKIP consistently outperforms JKH.

Thanks to the rapid development of mobile technologies, smartphones allow people to be reachable anywhere and anytime. In addition to the benefits for end users, researchers and developers can also benefit from the powerful devices that participants potentially carry on a daily basis. This minitrack workshop brings together researchers with an interest on using mobile devices as instruments to collect data and conduct mobile user studies, with a focus on understanding human-behavior, routines and gathering context.

The shared online calendar is the de facto standard for event organisation and management in the modern office environment. It is also a potentially valuable source of context, provided the calendar event data represent an accurate account of 'real-world' events. However, as we show through a field study, the calendar does not represent reality well as genuine events are hidden by a multitude of reminders and 'placeholders', i.e. events that appear in the calendar but do not occur. We show that the calendar's representation of real events can be significantly improved through data fusion with other sources of context, namely social network and location data. Finally, we discuss some of the issues raised during our field study, their significance and how performance could be farther improved.

"STiNG" is a Cache Coherent Non-Uniform Memory Access (CC-NUMA) Multiprocessor designed and built by Sequent Computer Systems, Inc. It combines four processor Symmetric Multi-processor (SMP) nodes (called Quads), using a Scalable Coherent Interface (SCI) based coherent interconnect. The Quads are based on the Intel P6 processor and the external bus it defines. In addition to 4 P6 processors, each Quad may contain up to 4 GBytes of system memory, 2 Peripheral Component Interface (PCI) busses for I/O, and a Lynx board. The Lynx board provides the datapath to the SCI-based interconnect and ensures system-wide cache coherency. STiNG is one of the first commercial CC-NUMA systems to be built. This paper describes the motivation for building STiNG as well as its architecture and implementation. In addition, performance analysis is provided for On-Line Transaction Processing (OLTP) and Decision Support System (DSS) workloads. Finally, the status of the current implementation is reviewed.