Learning to infer: RL-based search for DNN primitive selection on Heterogeneous Embedded Systems

Deep Learning is increasingly being adopted by industry for computer vision applications running on embedded devices. While Convolutional Neural Networks' accuracy has achieved a mature and remarkable state, inference latency and throughput are a major concern especially when targeting low-cost and low-power embedded platforms. CNNs' inference latency may become a bottleneck for Deep Learning adoption by industry, as it is a crucial specification for many real-time processes. Furthermore, deployment of CNNs across heterogeneous platforms presents major compatibility issues due to vendor-specific technology and acceleration libraries. In this work, we present QS-DNN, a fully automatic search based on Reinforcement Learning which, combined with an inference engine optimizer, efficiently explores through the design space and empirically finds the optimal combinations of libraries and primitives to speed up the inference of CNNs on heterogeneous embedded devices. We show that, an optimized combination can achieve 45x speedup in inference latency on CPU compared to a dependency-free baseline and 2x on average on GPGPU compared to the best vendor library. Further, we demonstrate that, the quality of results and time "to-solution" is much better than with Random Search and achieves up to 15x better results for a short-time search

Time fractals and discrete scale invariance with trapped ions

We show that a one-dimensional chain of trapped ions can be engineered to produce a quantum mechanical system with discrete scale invariance and fractal-like time dependence. By discrete scale invariance we mean a system that replicates itself under a rescaling of distance for some scale factor, and a time fractal is a signal that is invariant under the rescaling of time. These features are reminiscent of the Efimov effect, which has been predicted and observed in bound states of three-body systems. We demonstrate that discrete scale invariance in the trapped ion system can be controlled with two independently tunable parameters. We also discuss the extension to n-body states where the discrete scaling symmetry has an exotic heterogeneous structure. The results we present can be realized using currently available technologies developed for trapped ion quantum systems.Comment: 4 + 5 pages (main + supplemental materials), 2 + 3 figures (main + supplemental materials), version to appear in Physical Review A Rapid Communication

Rational bidding using reinforcement learning: an application in automated resource allocation

The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized

Functional organisation for verb generation in children with developmental language disorder

Developmental language disorder (DLD) is characterised by difficulties in learning one's native language for no apparent reason. These language difficulties occur in 7% of children and are known to limit future academic and social achievement. Our understanding of the brain abnormalities associated with DLD is limited. Here, we used a simple four-minute verb generation task (children saw a picture of an object and were instructed to say an action that goes with that object) to test children between the ages of 10–15 years (DLD N = 50, typically developing N = 67). We also tested 26 children with poor language ability who did not meet our criteria for DLD. Contrary to our registered predictions, we found that children with DLD did not have (i) reduced activity in language relevant regions such as the left inferior frontal cortex; (ii) dysfunctional striatal activity during overt production; or (iii) a reduction in left-lateralised activity in frontal cortex. Indeed, performance of this simple language task evoked activity in children with DLD in the same regions and to a similar level as in typically developing children. Consistent with previous reports, we found sub-threshold group differences in the left inferior frontal gyrus and caudate nuclei, but only when analysis was limited to a subsample of the DLD group (N = 14) who had the poorest performance on the task. Additionally, we used a two-factor model to capture variation in all children studied (N = 143) on a range of neuropsychological tests and found that these language and verbal memory factors correlated with activity in different brain regions. Our findings indicate a lack of support for some neurological models of atypical language learning, such as the procedural deficit hypothesis or the atypical lateralization hypothesis, at least when using simple language tasks that children can perform. These results also emphasise the importance of controlling for and monitoring task performance

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

Printed Nanostructures for Organic Photovoltaic Cells and Solution‐Processed Polymer Light‐Emitting Diodes

We review the progress on printing‐based technologies for organic electronic devices, especially organic photovoltaic (OPV) cells and polymer light‐emitting diodes (PLEDs). First we discuss recent efforts to introduce interdigitated nanostructures on the order of tens of nanometers to the photoactive layers of OPV cells using nanoimprint lithography including a soft‐printing process developed in our research group that can easily produce sub‐20 nm scale organic semiconductor nanopillars. Second, we review solution‐processible printing technologies such as gravure printing, screen printing, blade coating, and slot–die coating for high‐throughput manufacturing of PLEDs.Illuminating results: This article reviews the progress on printing‐based technologies for organic electronic devices, especially organic photovoltaic (OPV) cells and polymer light‐emitting diodes (PLEDs), including solution‐processible printing technologies such as gravure printing, screen printing, blade coating, and slot–die coating for high‐throughput manufacturing.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111088/1/340_ftp.pd

Using Nutrition for Intervention and Prevention against Environmental Chemical Toxicity and Associated Diseases

BACKGROUND: Nutrition and lifestyle are well-defined modulators of chronic diseases. Poor dietary habits (such as high intake of processed foods rich in fat and low intake of fruits and vegetables), as well as a sedentary lifestyle clearly contribute to today’s compromised quality of life in the United States. It is becoming increasingly clear that nutrition can modulate the toxicity of environmental pollutants. OBJECTIVES: Our goal in this commentary is to discuss the recommendation that nutrition should be considered a necessary variable in the study of human disease associated with exposure to environmental pollutants. DISCUSSION: Certain diets can contribute to compromised health by being a source of exposure to environmental toxic pollutants. Many of these pollutants are fat soluble, and thus fatty foods often contain higher levels of persistent organics than does vegetable matter. Nutrition can dictate the lipid milieu, oxidative stress, and antioxidant status within cells. The modulation of these parameters by an individual’s nutritional status may have profound affects on biological processes, and in turn influence the effects of environmental pollutants to cause disease or dysfunction. For example, potential adverse health effects associated with exposure to polychlorinated biphenyls may increase as a result of ingestion of certain dietary fats, whereas ingestion of fruits and vegetables, rich in antioxidant and anti-inflammatory nutrients or bioactive compounds, may provide protection. CONCLUSIONS: We recommend that future directions in environmental health research explore this nutritional paradigm that incorporates a consideration of the relationships between nutrition and lifestyle, exposure to environmental toxicants, and disease. Nutritional interventions may provide the most sensible means to develop primary prevention strategies of diseases associated with many environmental toxic insults

Ultrastructural differences between diabetic and idiopathic gastroparesis

The ultrastructural changes in diabetic and idiopathic gastroparesis are not well studied and it is not known whether there are different defects in the two disorders. As part of the Gastroparesis Clinical Research Consortium, full thickness gastric body biopsies from 20 diabetic and 20 idiopathic gastroparetics were studied by light microscopy. Abnormalities were found in many (83%) but not all patients. Among the common defects were loss of interstitial cells of Cajal (ICC) and neural abnormalities. No distinguishing features were seen between diabetic and idiopathic gastroparesis. Our aim was to provide a detailed description of the ultrastructural abnormalities, compare findings between diabetic and idiopathic gastroparesis and determine if patients with apparently normal immunohistological features have ultrastructural abnormalities. Tissues from 40 gastroparetic patients and 24 age‐ and sex‐matched controls were examined by transmission electron microscopy (TEM). Interstitial cells of Cajal showing changes suggestive of injury, large and empty nerve endings, presence of lipofuscin and lamellar bodies in the smooth muscle cells were found in all patients. However, the ultrastructural changes in ICC and nerves differed between diabetic and idiopathic gastroparesis and were more severe in idiopathic gastroparesis. A thickened basal lamina around smooth muscle cells and nerves was characteristic of diabetic gastroparesis whereas idiopathic gastroparetics had fibrosis, especially around the nerves. In conclusion, in all the patients TEM showed abnormalities in ICC, nerves and smooth muscle consistent with the delay in gastric emptying. The significant differences found between diabetic and idiopathic gastroparesis offers insight into pathophysiology as well as into potential targeted therapies.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92113/1/jcmm1451.pd

1,1-Dimethyl­biguanidium(2+) dinitrate

In the crystal structure of the title compound, C4H13N5 2+·2NO3 −, the main inter­molecular inter­actions are the N—H⋯O hydrogen bonds between the cationic amino groups and the O atoms of the nitrate ions. All amino H atoms and nitrate O atoms are involved in the three-dimensional hydrogen-bond network. There are two graph-set motifs R 2 2(8), which include the amino groups connected to the N atoms in the biguanide 3-, 4- and 5-positions, and the O atoms of a nitrate ion. They are extended along the a axis. An O atom of the second nitrate ion is involved in a graph-set motif C(4) that is a part of a helix-like N—H⋯O⋯H—N—H⋯O⋯ chain oriented along the b axis. There are also two weak C—H⋯O inter­actions in the crystal structure