Evaluation of Parameter-Scaling for Efficient Deep Learning on Small Satellites

Parameter-scaling techniques change the number of parameters in a machine-learning model in an effort to make the network more amenable to different device types or accuracy requirements. This research compares the performance of two such techniques. NeuralScale is a neural architecture search method which claims to generate deep neural networks for devices that are resource-constrained. It shrinks a network to a target number of parameters by adjusting the width of layers independently to achieve a higher accuracy than previous methods. The novel NeuralScale algorithm is compared to the baseline uniform scaling of MobileNet-style models, where the width of each layer in the model is scaled uniformly across the network. Measurements of the latency and runtime memory required for inference were gathered on the NVIDIA Jetson TX2 and Jetson AGX Xavier embedded GPUs using NVIDIA TensorRT. Measurements were also gathered on the Raspberry Pi 4 embedded CPU featuring ARM Cortex-A72 cores using ONNX Runtime. VGG-11, MobileNetV2, Pre-Activation ResNet-18, and ResNet-50 were all scaled to 0.25×, 0.50×, 0.75×, and 1.00× the original number of parameters. On embedded GPUs, this research finds that NeuralScale models do offer higher accuracy, but they run slower and consume much more runtime memory during inference than their equivalent uniform-scaling models. On average, NeuralScale is 40% as efficient as uniform scaling in terms of accuracy per megabyte of runtime memory, and NeuralScale uses 2.7× the runtime memory per parameter as uniform scaling. On the embedded CPU, NeuralScale is slightly more efficient than uniform scaling in terms of accuracy per megabyte of memory, using essentially the same amount of memory per parameter. However, there is on average an over 2.5× increase in the latency for inference. Importantly, parameter count does not guarantee performance in terms of runtime-memory usage between the scaling methods on embedded GPUs, while latency grows significantly on embedded CPUs

Application Software of the Future-Filter Design with Gem

As the use of computer in engineering design as well as other areas increase, it becomes more imperative that the application software used be as simple, convenient, and powerful as possible. The engineer is not interested in the internal workings of the computer or its operating system. It is the design itself that takes precedence. The filter design package developed for this project, known as FILTER, is such an application. With FILTER, coupled with the Digital Research Graphics Environment Manager, the engineer is led through the analog and digital filter design phase on a personal computer with carefully designed interactive computer graphics requiring little or no computer knowledge

The effects of a home-based physical activity intervention on cardiorespiratory fitness in breast cancer survivors; a randomised controlled trial

The aim of this current randomised controlled trial was to evaluate the effects of a home-based physical activity (PA) intervention on cardiorespiratory fitness in breast cancer survivors. Thirty-two post-adjuvant therapy breast cancer survivors (age = 52 ± 10 years; BMI = 27.2 ± 4.4 kg∙m2) were randomised to a six-month home-based PA intervention with face-to-face and telephone PA counselling or usual care. Cardiorespiratory fitness and self-reported PA were assessed at baseline and at six-months. Participants had a mean relative V̇O2max of 25.3 ± 4.7 ml∙kg−1∙min−1, which is categorised as “poor” according to age and gender matched normative values. Magnitude-based inference analyses revealed likely at least small beneficial effects (effect sizes ≥.20) on absolute and relative V̇O2 max (d = .44 and .40, respectively), and total and moderate PA (d = .73 and .59, respectively) in the intervention compared to the usual care group. We found no likely beneficial improvements in any other outcome. Our home-based PA intervention led to likely beneficial, albeit modest, increases in cardiorespiratory fitness and self-reported PA in breast cancer survivors. This intervention has the potential for widespread implementation and adoption, which could considerably impact on post-treatment recovery in this population

Celebrating HICSS50: The Past, Present, and Future of HICSS

The Hawaii International Conference on System Sciences (HICSS) celebrated its 50th anniversary (HICSS-50) in January, 2017. To mark the occasion and to pay respect to the significant standing of this conference in the global IS community, the Communications of the Association for Information Systems (CAIS) organized a special section on “Celebrating HICSS50: The Past, Present, and Future of HICSS Conference”. In this editorial, we share the guest editors’ perspectives on HICSS and summarize the three papers in the special section

WP 2016-337

Means testing can balance the need to provide adequate retirement incomes with the requirement that such provision is fiscally sustainable and economically efficient. Critics of the policy suggest that to reduce benefits as a retiree’s income and/or wealth increase is to discourage work and savings. Yet such distortions are small compared to those resulting from large earnings related pensions that, due to demographic change, require greater levels of financing via payroll taxes. Some form of means testing exists in most countries, usually involving small, safety-net schemes that target the poorest retirees (e.g., the Supplemental Security Income program in the U.S.). But an appropriately designed means-testing instrument can also be used to reduce the liability of large, publicly financed social security promises by excluding the affluent. This paper summarises means-testing design and implementation in a number of OECD countries as well as tackling key criticisms of means testing. In doing so, we discuss a number of recent, cutting-edge modelling approaches and empirical insights that examine economic impacts of means testing in the Australian and U.S. contexts.Social Security Administrationhttp://deepblue.lib.umich.edu/bitstream/2027.42/121944/1/wp337.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/121944/4/wp337.pdfDescription of wp337.pdf : Working paperDescription of wp337.pdf : Working pape

Neutron-Induced, Single-Event Effects on Neuromorphic Event-based Vision Sensor: A First Step Towards Space Applications

This paper studies the suitability of neuromorphic event-based vision cameras for spaceflight, and the effects of neutron radiation on their performance. Neuromorphic event-based vision cameras are novel sensors that implement asynchronous, clockless data acquisition, providing information about the change in illuminance greater than 120dB with sub-millisecond temporal precision. These sensors have huge potential for space applications as they provide an extremely sparse representation of visual dynamics while removing redundant information, thereby conforming to low-resource requirements. An event-based sensor was irradiated under wide-spectrum neutrons at Los Alamos Neutron Science Center and its effects were classified. We found that the sensor had very fast recovery during radiation, showing high correlation of noise event bursts with respect to source macro-pulses. No significant differences were observed between the number of events induced at different angles of incidence but significant differences were found in the spatial structure of noise events at different angles. The results show that event-based cameras are capable of functioning in a space-like, radiative environment with a signal-to-noise ratio of 3.355. They also show that radiation-induced noise does not affect event-level computation. We also introduce the Event-based Radiation-Induced Noise Simulation Environment (Event-RINSE), a simulation environment based on the noise-modelling we conducted and capable of injecting the effects of radiation-induced noise from the collected data to any stream of events in order to ensure that developed code can operate in a radiative environment. To the best of our knowledge, this is the first time such analysis of neutron-induced noise analysis has been performed on a neuromorphic vision sensor, and this study shows the advantage of using such sensors for space applications

Specific inhibition of binding of antistasin and [A103,106,108] antistasin 93–119 to sulfatide (Gal(3-SO4)β1-1Cer) by glycosaminoglycans

AbstractLeech-derived antistasin is a potent anticoagulant and antimetastatic protein that binds sulfatide (Gal(3-SO4)β1-1Cer)and sulfated polysaccharides. In this study, the synthetic fragment [A103,106,108] antistasin 93–119, which corresponds to the carboxyl terminus, showed specific and saturable binding to sulfatide. Binding was competitively blocked by glycosaminoglycans (GAGs) in the order: dextran sulfate 5000 ≅ dextran sulfate 500 0OO > heparin > dermatan sulfate ⪢ chondroitin sulfates A and C. This rank order of inhibitory potency was identical to that observed with whole antistasin. We suggest that residues 93–119 of antistasin represent a critical domain for binding GAGs and sulfated glycolipids