Robotic manipulation in cluttered environments presents significant challenges, particularly when the clutter includes thin, deformable objects like cables, which complicate perception and decision-making processes. In the context of datacenters, the automation of networking tasks often involves the manipulation of optical transceivers within densely packed cable configurations. Such environments are characterized by an abundance of delicate, overlapping, and intersecting cables, leading to frequent occlusions. This paper introduces an innovative system designed for the manipulation of optical transceivers in environments cluttered by cables. Our integrated approach combines advanced 3D scene understanding with a heuristic-based pushing policy to effectively manipulate optical transceivers amidst clutter. The system's perception component utilizes image segmentation and 3D reconstruction to accurately model the transceivers and surrounding cables. Meanwhile, the planning aspect employs a search algorithm with task-specific heuristics, to navigate the gripper, displace obstructing cables, and safely achieve a precise pre-grasp position in front of the target transceiver. We have conducted extensive evaluations of our methodology in both simulated and real-world settings, demonstrating its high success rates, robustness, and proficiency in addressing the unique challenges posed by cable-occluded environments within datacenters.

Sustainable and cost-effective long-term storage remains an unsolved problem. The most widely used storage technologies today are magnetic (hard disk drives and tape). They use media that degrades over time and has a limited lifetime, which leads to inefficient, wasteful, and costly solutions for long-lived data. This article presents Silica: the first cloud storage system for archival data underpinned by quartz glass, an extremely resilient media that allows data to be left in situ indefinitely. The hardware and software of Silica have been co-designed and co-optimized from the media up to the service level with sustainability as a primary objective. The design follows a cloud-first, data-driven methodology underpinned by principles derived from analyzing the archival workload of a large public cloud service. Silica can support a wide range of archival storage workloads and ushers in a new era of sustainable, cost-effective storage.

The vision of self-maintaining systems is to make cloud hardware automatically servicing and repairing using robotics. We define a self-maintaining system as one where software can control robotics that can automatically perform hardware maintenance tasks and repair operations. This reduces failure service windows and lowers the risk of repairs causing further cascading failures and outages. Self-maintaining systems are not purely reactive to failures, but also do proactive maintenance before failures occur which reduces future hardware failures. Operating an entire datacenter as a self-maintaining system is many years away, and we present four stages of automation, analogous to levels used for autonomous vehicles, required to reach the full vision for datacenters.

Automated storage and retrieval systems (ASRS) are a key component of the modern storage industry, and are used in a wide range of applications, carrying anything from lightweight tape cartridges to entire pallets of goods. Many of these systems are under pressure to maximise the use of space by growing in height and density, but this can create challenges for the the robots that service them. In this context, we present RASCAL, a novel ASRS robot for small payload items in structured environments, with a focus on system-level scalability and redundancy. We describe the design objectives of RASCAL and how they address some of the limitations of existing robotic systems in this area, such as scalability and redundancy. We then demonstrate the viability of our design with a proof-of-concept implementation of a data centre storage media robot, and show through a series of experiments that its design, speed, accuracy, and energy efficiency are appropriate for this application.

Gravitationally lensed quasars are important objects in astronomy for probing the universe. Unfortunately, these objects are exceedingly rare, occurring only for only ∼ 1/10 000 quasars. The challenge is to find these lensed quasars amongst large astronomical data sets. In contrast to previous attempts, which have only made use of numeric data, we perform semi-supervised classification based on images of quasars. These images are low resolution and noisy, but are enough for experienced astronomers to perform classification. Using virtual adversarial training to take advantage of millions of unlabelled images, we develop a classifier which achieves an F1 score of 0.49 — an extremely impressive result in this domain. Predictions made by this classifier are already being used to select candidates for telescopes around the world.

Sustainable and cost-effective long-term storage remains an unsolved problem. The most widely used storage technologies today are magnetic (hard disk drives and tape). They use media that degrades over time and has a limited lifetime, which leads to inefficient, wasteful, and costly solutions for long-lived data. This paper presents Silica: the first cloud storage system for archival data underpinned by quartz glass, an extremely resilient media that allows data to be left in situ indefinitely. The hardware and software of Silica have been co-designed and co-optimized from the media up to the service level with sustainability as a primary objective. The design follows a cloud-first, data-driven methodology underpinned by principles derived from analyzing the archival workload of a large public cloud service. Silica can support a wide range of archival storage workloads and ushers in a new era of sustainable, cost-effective storage.

We explore the design space of energy-neutral situated displays, which give physical presence to digital information. We investigate three central dimensions: energy sources, display technologies, and wireless communications. Based on the power implications from our analysis, we present a thin, wireless, photovoltaic-powered display that is quick and easy to deploy and capable of indefinite operation in indoor lighting conditions. The display uses a low-resolution e-paper architecture, which is 35 times more energy-efficient than smaller-sized high-resolution displays. We present a detailed analysis on power consumption, photovoltaic energy harvesting performance, and a detailed comparison to other display-driving architectures. Depending on the ambient lighting, the display can trigger an update every 1 -- 25 minutes and communicate to a PC or smartphone via Bluetooth Low-Energy.

Fully articulated hand tracking promises to enable fundamentally new interactions with virtual and augmented worlds, but the limited accuracy and efficiency of current systems has prevented widespread adoption. Today's dominant paradigm uses machine learning for initialization and recovery followed by iterative model-fitting optimization to achieve a detailed pose fit. We follow this paradigm, but make several changes to the model-fitting, namely using: (1) a more discriminative objective function; (2) a smooth-surface model that provides gradients for non-linear optimization; and (3) joint optimization over both the model pose and the correspondences between observed data points and the model surface. While each of these changes may actually increase the cost per fitting iteration, we find a compensating decrease in the number of iterations. Further, the wide basin of convergence means that fewer starting points are needed for successful model fitting. Our system runs in real-time on CPU only, which frees up the commonly over-burdened GPU for experience designers. The hand tracker is efficient enough to run on low-power devices such as tablets. We can track up to several meters from the camera to provide a large working volume for interaction, even using the noisy data from current-generation depth cameras. Quantitative assessments on standard datasets show that the new approach exceeds the state of the art in accuracy. Qualitative results take the form of live recordings of a range of interactive experiences enabled by this new approach.

We present findings from a year-long engagement with a street and its community. The work explores how the production and use of data is bound up with place, both in terms of physical and social geography. We detail three strands of the project. First, we consider how residents have sought to curate existing data about the street in the form of an archive with physical and digital components. Second, we report endeavours to capture data about the street's environment, especially of vehicle traffic. Third, we draw on the possibilities afforded by technologies for polling opinion. We reflect on how these engagements have: materialised distinctive relations between the community and their data; surfaced flows and contours of data, and spatial, temporal and social boundaries; and enacted a multiplicity of 'small worlds'. We consider how such a conceptualisation of data-in-place is relevant to the design of technology.

We present a machine learning technique for estimating absolute, per-pixel depth using any conventional monocular 2D camera, with minor hardware modifications. Our approach targets close-range human capture and interaction where dense 3D estimation of hands and faces is desired. We use hybrid classification-regression forests to learn how to map from near infrared intensity images to absolute , metric depth in real-time. We demonstrate a variety of human-computer interaction and capture scenarios. Experiments show an accuracy that outperforms a conventional light fall-off baseline, and is comparable to high-quality consumer depth cameras, but with a dramatically reduced cost, power consumption, and form-factor.