Reading Globalization from the Margin: The Case of Abdullah Munshi

In this essay I argue that the global perspective, established in the era of modernEuropean imperialism, is given institutional expression as a way of seeing that is engaged—both by ruler and ruled— as the frame of adequate representation. Briefly outlining how this frame operates in historical and cultural studies today, I examine its deployment in mid-nineteenth-century Melaka and Singapore through a reading of the Hikayat Abdullah, a seminal Malay-language text composed by Abdullah bin Abdul Kadir. Although Abdullah self-consciously sets about reproducing the global perspective, I show how this mode of thematization is interrupted and displaced as it brings about an encounter between the diverse and uneven contexts of the native and European worlds

SPRK: A Low-Cost Stewart Platform For Motion Study In Surgical Robotics

To simulate body organ motion due to breathing, heart beats, or peristaltic movements, we designed a low-cost, miniaturized SPRK (Stewart Platform Research Kit) to translate and rotate phantom tissue. This platform is 20cm x 20cm x 10cm to fit in the workspace of a da Vinci Research Kit (DVRK) surgical robot and costs $250, two orders of magnitude less than a commercial Stewart platform. The platform has a range of motion of +/- 1.27 cm in translation along x, y, and z directions and has motion modes for sinusoidal motion and breathing-inspired motion. Modular platform mounts were also designed for pattern cutting and debridement experiments. The platform's positional controller has a time-constant of 0.2 seconds and the root-mean-square error is 1.22 mm, 1.07 mm, and 0.20 mm in x, y, and z directions respectively. All the details, CAD models, and control software for the platform is available at github.com/BerkeleyAutomation/sprk

EdgeServe: An Execution Layer for Decentralized Prediction

The relevant features for a machine learning task may be aggregated from data sources collected on different nodes in a network. This problem, which we call decentralized prediction, creates a number of interesting systems challenges in managing data routing, placing computation, and time-synchronization. This paper presents EdgeServe, a machine learning system that can serve decentralized predictions. EdgeServe relies on a low-latency message broker to route data through a network to nodes that can serve predictions. EdgeServe relies on a series of novel optimizations that can tradeoff computation, communication, and accuracy. We evaluate EdgeServe on three decentralized prediction tasks: (1) multi-camera object tracking, (2) network intrusion detection, and (3) human activity recognition.Comment: 13 pages, 8 figure

Quantifying Uncertainty in Aggregate Queries over Integrated Datasets

Data integration is a notoriously difficult and heuristic-driven process, especially when ground-truth data are not readily available. This paper presents a measure of uncertainty by providing maximal and minimal ranges of a query outcome in two-table, one-to-many data integration workflows. Users can use these query results to guide a search through different matching parameters, similarity metrics, and constraints. Even though there are exponentially many such matchings, we show that in appropriately constrained circumstances that this result range can be calculated in polynomial time with bipartite graph matching. We evaluate this on real-world datasets and synthetic datasets, and find that uncertainty estimates are more robust when a graph-matching based approach is used for data integration