Following instructions in a virtual school: Does working memory play a role?

Accumulating evidence that working memory supports the ability to follow instructions has so far been restricted to experimental paradigms that have greatly simplified the practical demands of performing actions to instructions in everyday tasks. The aim of the present study was to investigate whether working memory is involved in maintaining information over the longer periods of time that are more typical of everyday situations that require performing instructions to command. Forty-two children 7–11 years of age completed assessments of working memory, a real-world following-instructions task employing 3-D objects, and two new computerized instruction-following tasks involving navigation around a virtual school to complete a sequence of practical spoken commands. One task involved performing actions in a single classroom, and the other, performing actions in multiple locations in a virtual school building. Verbal working memory was closely linked with all three following-instructions paradigms, but with greater association to the virtual than to the real-world tasks. These results indicate that verbal working memory plays a key role in following instructions over extended periods of activity.

Heterogeneous error-resilient scheme for spectral analysis in ultra-low power wearable electrocardiogram devices

Wearable devices performing advanced bio-signal analysis algorithms are aimed to foster a revolution in healthcare provision of chronic cardiac diseases. In this context, energy efficiency is of paramount importance, as long-term monitoring must be ensured while relying on a tiny power source. Operating at a scaled supply voltage, just above the threshold voltage, effectively helps in saving substantial energy, but it makes circuits, and especially memories, more prone to errors, threatening the correct execution of algorithms. The use of error detection and correction codes may help to protect the entire memory content, however it incurs in large area and energy overheads which may not be compatible with the tight energy budgets of wearable systems. To cope with this challenge, in this paper we propose to limit the overhead of traditional schemes by selectively detecting and correcting errors only in data highly impacting the end-to-end quality of service of ultra-low power wearable electrocardiogram (ECG) devices. This partition adopts the protection of either significant words or significant bits of each data element, according to the application characteristics (statistical properties of the data in the application buffers), and its impact in determining the output. The proposed heterogeneous error protection scheme in real ECG signals allows substantial energy savings (11% in wearable devices) compared to state-of-the-art approaches, like ECC, in which the whole memory is protected against errors. At the same time, it also results in negligible output quality degradation in the evaluated power spectrum analysis application of ECG signals.

Perceptual learning of acoustic noise generates memory-evoked potentials

Experience continuously imprints on the brain at all stages of life. The traces it leaves behind can produce perceptual learning [1], which drives adaptive behavior to previously encountered stimuli. Recently, it has been shown that even random noise, a type of sound devoid of acoustic structure, can trigger fast and robust perceptual learning after repeated exposure [2]. Here, by combining psychophysics, electroencephalography (EEG), and modeling, we show that the perceptual learning of noise is associated with evoked potentials, without any salient physical discontinuity or obvious acoustic landmark in the sound. Rather, the potentials appeared whenever a memory trace was observed behaviorally. Such memory-evoked potentials were characterized by early latencies and auditory topographies, consistent with a sensory origin. Furthermore, they were generated even on conditions of diverted attention. The EEG waveforms could be modeled as standard evoked responses to auditory events (N1-P2) [3], triggered by idiosyncratic perceptual features acquired through learning. Thus, we argue that the learning of noise is accompanied by the rapid formation of sharp neural selectivity to arbitrary and complex acoustic patterns, within sensory regions. Such a mechanism bridges the gap between the short-term and longer-term plasticity observed in the learning of noise [2, 4-6]. It could also be key to the processing of natural sounds within auditory cortices [7], suggesting that the neural code for sound source identification will be shaped by experience as well as by acoustics.

Heat Activated Prestressing of Shape Memory Alloys for Active Confinement of Concrete Sections

This paper presents the results from the experimental investigation on heat activated prestressing of Shape Memory Alloy (SMA) wires for active confinement of concrete sections. Active confinement of concrete is found to be much more effective than passive confinement which becomes effective only when the concrete starts to dilate. Active confinement achieved using conventional prestressing techniques often faces many obstacles due to practical limitations. A class of smart materials that has recently drawn attention in civil engineering is the super elastic SMA which has the ability to undergo reversible hysteretic shape change known as the shape memory effect. The shape memory effect of SMAs can be utilized to develop a convenient prestressing technique for active confinement of concrete sections.
In this study a series of experimental tests are conducted to study Heat Activated Prestress (HAP) in SMAs. Three different types of tests are conducted with different loading protocol to determine parameters such as HAP, residual strain after heating and range of strain that can be used for effective active confinement after HAP. Test results show a maximum HAP of about 500 MPa can be achieved after heating and approximately 450MPa is retained at 25oC in specimens pre-strained by 6%. A substantial amount of strain recovery upon unloading and after heating the SMA wires is recorded. About 2.5% elastic strain recovery upon unloading from 6% strain level is observed. In the specimen pre-strained by 6%, a total of 4% strain is recovered when unloaded after heating. A strain range of 3% is found available for effective confinement after HAP. Test results demonstrate that SMAs have unique features that can be intelligently employed in many civil engineering applications including active confinement of concrete sections.

Peace by Piece: (Re) imagining Division in Belfast’s Contested Spaces through Memory

This paper investigates processes and actions of diversifying memories of division in Northern Ireland’s political conflict known as the Troubles. Societal division is manifested in its built fabric and territories that have been adopted by predominant discourses of a fragmented society in Belfast; the unionist east and the nationalist west. The aim of the paper is to explore current approaches in planning contested spaces that have changed over time, leading to success in many cases. The argument is that divided cities, like Belfast, feature spatial images and memories of division that range from physical, clear-cut segregation to manifested actions of violence and have become influential representations in the community’s associative memory. While promoting notions of ‘re-imaging’ by current councils demonstrates a total erasure of the Troubles through cleansing its local collective memory, there yet remains an attempt to communicate a different tale of the city’s socio-economic past, to elaborate its supremacy for shaping future lived memories. Yet, planning Belfast’s contested areas is still suffering from a poor understanding of the context and its complexity against overambitious visions. 

Corrosion Fatigue Behaviour of Laser-welded Shape Memory NiTi Wires in a Simulated Body Fluid

Corrosion fatigue is a fracture process as a consequence of synergistic interactions between the material structure, corrosive environment and cyclic loads/strains. It is difficult to be detected and can cause unexpected failure of engineering components in use. This study reveals a comparison of corrosion fatigue behaviour of laser-welded and bare NiTi wires using bending rotation fatigue (BRF) test coupled with a specifically-designed corrosion cell. The testing medium was Hanks’ solution (simulated body fluid) at 37.5 oC. Electrochemical impedance spectroscopic (EIS) measurement was carried out to monitor the change of corrosion resistance of sample during the BRF test at different periods of time. Experiments indicate that the laser-welded NiTi wire would be more susceptible to the corrosion fatigue attack than the bare NiTi wire. This study can serve as a benchmark for the product designers and engineers to understand the corrosion fatigue behaviour of the NiTi laser weld joint and determine the fatigue life safety factor for NiTi medical devices/implants involving laser welding in the fabrication process.

Energy Optimization of Memory Intensive Parallel Workloads

Energy consumption is an important concern in modern multicore processors. The energy consumed by a multicore processor during the execution of an application can be minimized by tuning the hardware state utilizing knobs such as frequency, voltage etc. The existing theoretical work on energy minimization using Global DVFS (Dynamic Voltage and Frequency Scaling), despite being thorough, ignores the time and the energy consumed by the CPU on memory accesses and the dynamic energy consumed by the idle cores. This article presents an analytical energy-performance model for parallel workloads that accounts for the time and the energy consumed by the CPU chip on memory accesses in addition to the time and energy consumed by the CPU on CPU instructions. In addition, the model we present also accounts for the dynamic energy consumed by the idle cores. The existing work on global DVFS for parallel workloads shows that using a single frequency for the entire duration of a parallel application is not energy optimal and that varying the frequency according to the changes in the parallelism of the workload can save energy. We present an analytical framework around our energy-performance model to predict the operating frequencies (that depend upon the amount of parallelism) for global DVFS that minimize the overall CPU energy consumption. We show how the optimal frequencies in our model differ from the optimal frequencies in a model that does not account for memory accesses. We further show how the memory intensity of an application affects the optimal frequencies.

Managed Acceleration for In-Memory Database Analytic Workloads

In-Memory Databases (IMDBs), such as SAP HANA, enable new levels of database performance by removing the disk bottleneck and by compressing data in memory. The consequence of this improved performance means that reports and analytic queries can now be processed on demand. Therefore, the goal is now to provide near real-time responses to compute and data intensive analytic queries. To facilitate this, much work has investigated the use of acceleration technologies within the database context. While current research into the application of these technologies has yielded positive results, they have tended to focus on single database tasks or on isolated single user requests. This paper uses SHEPARD, a framework for managing accelerated tasks across shared heterogeneous resources, to introduce acceleration into an IMDB. Results show how, using SHEPARD, multiple simultaneous user queries all receive speed-up by using a shared pool of accelerators. Results also show that offloading analytic tasks onto accelerators can have indirect benefits for other database workloads by reducing contention for CPU resources.