Actors: The Ideal Abstraction for Programming Kernel-Based Concurrency

GPU and multicore hardware architectures are commonly used in many different application areas to accelerate problem solutions relative to single CPU architectures. The typical approach to accessing these hardware architectures requires embedding logic into the programming language used to construct the application; the two primary forms of embedding are: calls to API routines to access the concurrent functionality, or pragmas providing concurrency hints to a language compiler such that particular blocks of code are targeted to the concurrent functionality. The former approach is verbose and semantically bankrupt, while the success of the latter approach is restricted to simple, static uses of the functionality. Actor-based applications are constructed from independent, encapsulated actors that interact through strongly-typed channels. This paper presents a first attempt at using actors to program kernels targeted at such concurrent hardware. Besides the glove-like fit of a kernel to the actor abstraction, quantitative code analysis shows that actor-based kernels are always significantly simpler than API-based coding, and generally simpler than pragma-based coding. Additionally, performance measurements show that the overheads of actor-based kernels are commensurate to API-based kernels, and range from equivalent to vastly improved for pragma-based annotations, both for sample and real-world applications

Supersensors: Raspberry Pi Devices for Smart Campus Infrastructure

We describe an approach for developing a campus-wide sensor network using commodity single board computers. We sketch various use cases for environmental sensor data, for different university stakeholders. Our key premise is that supersensors -- sensors with significant compute capability -- enable more flexible data collection, processing and reaction. In this paper, we describe the initial prototype deployment of our supersensor system in a single department at the University of Glasgow

Leveraging Diffusion-Based Image Variations for Robust Training on Poisoned Data

Backdoor attacks pose a serious security threat for training neural networks as they surreptitiously introduce hidden functionalities into a model. Such backdoors remain silent during inference on clean inputs, evading detection due to inconspicuous behavior. However, once a specific trigger pattern appears in the input data, the backdoor activates, causing the model to execute its concealed function. Detecting such poisoned samples within vast datasets is virtually impossible through manual inspection. To address this challenge, we propose a novel approach that enables model training on potentially poisoned datasets by utilizing the power of recent diffusion models. Specifically, we create synthetic variations of all training samples, leveraging the inherent resilience of diffusion models to potential trigger patterns in the data. By combining this generative approach with knowledge distillation, we produce student models that maintain their general performance on the task while exhibiting robust resistance to backdoor triggers.Comment: 11 pages, 3 tables, 2 figure

FPGA port of a large scientific model from legacy code: the Emanuel convection scheme

The potential of FPGAs for High-Performance Computing is increasingly recognized, but most work focuses on acceleration of small, isolated kernels. We present a parallel FPGA implementation of a legacy algorithm, the seminal scheme for cumulus convection in large-scale models developed by Emanuel [1]. Our design makes use of pipelines both at the arithmetic and at the logical stage level, keeping the entire algorithm on the FPGA. We assert that modern FPGAs have the resources to support this type of large algorithms. Through a practical and theoretical evaluation of our design we show how such an FPGA implementation compares to GPU implementations or multi-core approaches such as OpenMP

A survey of co-located multi-device audio experiences

Complimentary multi-device audio experiences are becoming increasingly common through the proliferation of mobile computing and the creation of bespoke multi-device audio production tools. However, the best use cases and design practices for these experiences remain less well understood. A review of multi-device audio experiences is therefore necessary to capture and consolidate the knowledge in this research area, and to move toward a set of design guidelines for creating more effective and engaging experiences. In this study, the range of applications of co-located multi-device audio experiences is explored and documented through a review of the literature and a survey, resulting in a dataset containing 31 individual experiences and 11 enabling tools or platforms. An initial analysis of the survey data revealed the frequency of types of devices and the forms of interaction in the experiences and platforms of the dataset

Design dimensions of co-located multi-device audio experiences

The widespread distribution of mobile computing presents new opportunities for the consumption of interactive and immersive media experiences using multiple connected devices. Tools now exist for the creation of these experiences; however, there is still limited understanding of the best design practices and use cases for the technology, especially in the context of audio experiences. In this study, the application space of co-located multi-device audio experiences is explored and documented through a review of the literature and a survey. Using the obtained information, a set of seven design dimensions that can be used to characterise and compare experiences of this type are proposed; these are synchronisation, context, position, relationship, interactivity, organisation, and distribution. A mapping of the current application space is presented where four categories are identified using the design dimensions. Finally, the overlap between co-located multi-device audio and audio augmented reality (AAR) experiences is highlighted and discussed. This work will contribute to the wider discussion about the role of multiple devices in audio experiences, and provide a source of reference for the design of future multi-device audio experiences

Matrix Supported Poly(2-oxazoline)-Based Hydrogels for DNA Catch and Release

We describe the synthesis of matrix supported hydrogel structures based on amine containing poly­(2-oxazoline)­s and their use to bind and release genetic material for potential applications in diagnostics or pathogen detection. Amine containing poly­(2-oxazoline)­s were synthesized by copolymerization of 2-ethyl-2-oxazoline with a monomer bearing a <i>tert</i>-butyl oxycarbonyl (Boc) protected amine group in the 2-position and subsequent deprotection. The statistical copolymers were used to generate hydrogels and matrix supported hydrogels by cross-linking of a certain fraction of the amine groups with epichlorhydrin. Supported structures were prepared by soaking porous polyethylene (PE) or polypropylene (PP) filter materials in a copolymer/epichlorhydrin solution, which was cross-linked upon heating. Scanning electron microscopy (SEM) of the composites revealed a bead like structure of the gel phase, which could be attributed to a lower critical solution temperature (LCST) behavior of the initial polymer prior to gelation. The dependency of the LCST behavior on the content of amine groups was investigated. Swelling values and the ratio of hydrogel per composite was determined using water sorption analysis. Subsequently, the ability of the systems to absorb and release labeled DNA was tested. Uptake and stimulated release, triggered by changes in pH, temperature, and heparin concentration, were investigated using fluorescence microscopy. Polymerase chain reaction (PCR) proved the successful recovery of the DNA, demonstrating the potential of the presented system for a broad range of molecular biological applications

A femtosecond X-ray/optical cross-correlator

For a fundamental understanding of ultra fast dynamics in chemistry, biology and materials science it has been a longstanding dream to record a molecular movie, where both the atomic trajectories and the chemical state of every atom in matter is followed in real time. Free-electron lasers (FEL) provide this perspective as they deliver brilliant femtosecond X-ray pulses spanning a wide photon energy range, which is necessary to gather element-specific and chemical-state-selective information with femtosecond time resolution. The key challenge lies in synchronizing the FEL with separate optical lasers. We exploit the peak brilliance of the FEL in Hamburg (FLASH) and establish X-ray pulse induced transient changes of the optical reflectivity in GaAs as a powerful tool for X-ray/optical cross-correlation. This constitutes a breakthrough en route to a molecular movie and – equally important – opens the novel field of femtosecond X-ray induced dynamics, only accessible with high brilliance X-ray free-electron lasers