3,770 research outputs found
Quality aware selective ECC for approximate DRAM
Approximate DRAMs are DRAM memories where energy saving techniques have been implemented by trading off bit-cell error rate with power consumption. They are considered part of the building blocks in the larger area of approximate computing. Relaxing refresh rate has been proposed as an interesting solution to achieve better efficiency at the expense of rising error rate. However, some works have demonstrated that much better results are achieved if at word-level some bits are retained without errors (i.e. their cells are refreshed at nominal rate), resulting in architectures using multiple refresh rates. In this paper we present a technique that can be applied to approximate DRAMs under reduced refresh rate. It allows to trim error rate at word-level, while still performing the refresh operation at the same rate for all cells. The number of bits that are protected is configurable and depends on output quality degradation that can be accepted by the application
Customizable vector acceleration in extreme-edge computing. A risc-v software/hardware architecture study on VGG-16 implementation
Computing in the cloud-edge continuum, as opposed to cloud computing, relies on high performance processing on the extreme edge of the Internet of Things (IoT) hierarchy. Hardware acceleration is a mandatory solution to achieve the performance requirements, yet it can be tightly tied to particular computation kernels, even within the same application. Vector-oriented hardware acceleration has gained renewed interest to support artificial intelligence (AI) applications like convolutional networks or classification algorithms. We present a comprehensive investigation of the performance and power efficiency achievable by configurable vector acceleration subsystems, obtaining evidence of both the high potential of the proposed microarchitecture and the advantage of hardware customization in total transparency to the software program
Bunching visibility for correlated photons from single GaAs quantum dots
We study photon bunching phenomena associated with biexciton-exciton cascade
in single GaAs self-assembled quantum dots. Experiments carried out with a
pulsed excitation source show that significant bunching is only detectable at
very low excitation, where the typical intensity of photon streams is less than
the half of their saturation value. Our findings are qualitatively understood
with a model which accounts for Poissonian statistics in the number of
excitons, predicting the height of a bunching peak being determined by the
inverse of probability of finding more than one exciton.Comment: 6 pages, 6 figs to appear in Phys. Rev.
Comparison performance of visible-nir and near-infrared hyperspectral imaging for prediction of nutritional quality of goji berry (Lycium barbarum l.)
The potential of hyperspectral imaging for the prediction of the internal composition of goji berries was investigated. The prediction performances of models obtained in the Visible-Near Infrared (VIS-NIR) (400–1000 nm) and in the Near Infrared (NIR) (900–1700 nm) regions were compared. Analyzed constituents included Vitamin C, total antioxidant, phenols, anthocyanin, soluble solids content (SSC), and total acidity (TA). For vitamin C and AA, partial least square regression (PLSR) combined with different data pretreatments and wavelength selection resulted in a satisfactory prediction in the NIR region obtaining the R2pred value of 0.91. As for phenols, SSC, and TA, a better performance was obtained in the VIS-NIR region yielding the R2pred values of 0.62, 0.94, and 0.84, respectively. However, the prediction of total antioxidant and anthocyanin content did not give satisfactory results. Conclusively, hyperspectral imaging can be a useful tool for the prediction of the main constituents of the goji berry (Lycium barbarum L.)
Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change
This Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) has been jointly coordinated by Working Groups I (WGI) and II (WGII) of the Intergovernmental Panel on Climate Change (IPCC). The report focuses on the relationship between climate change and extreme weather and climate events, the impacts of such events, and the strategies to manage the associated risks. The IPCC was jointly established in 1988 by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP), in particular to assess in a comprehensive, objective, and transparent manner all the relevant scientific, technical, and socioeconomic information to contribute in understanding the scientific basis of risk of human-induced climate change, the potential impacts, and the adaptation and mitigation options. Beginning in 1990, the IPCC has produced a series of Assessment Reports, Special Reports, Technical Papers, methodologies, and other key documents which have since become the standard references for policymakers and scientists.This Special Report, in particular, contributes to frame the challenge of dealing with extreme weather and climate events as an issue in decisionmaking under uncertainty, analyzing response in the context of risk management. The report consists of nine chapters, covering risk management; observed and projected changes in extreme weather and climate events; exposure and vulnerability to as well as losses resulting from such events; adaptation options from the local to the international scale; the role of sustainable development in modulating risks; and insights from specific case studies
Coalitions in International Litigation: A Network Perspective
We apply network science principles to analyze the coalitions formed by
European Union (EU) nations and institutions during litigation proceedings at
the European Court of Justice. By constructing Friends and Foes networks, we
explore their characteristics and dynamics through the application of cluster
detection, motif analysis, and duplex analysis. Our findings demonstrate that
the Friends and Foes networks exhibit disassortative behavior, highlighting the
inclination of nodes to connect with dissimilar nodes. Furthermore, there is a
correlation among centrality measures, indicating that member states and
institutions with a larger number of connections play a prominent role in
bridging the network. An examination of the modularity of the networks reveals
that coalitions tend to align along regional and institutional lines, rather
than national government divisions. Additionally, an analysis of triadic binary
motifs uncovers a greater level of reciprocity within the Foes network compared
to the Friends network.Comment: 13 pages 11 figures, style and bibtex files include
Using chemometrics to characterise and unravel the near infra-red spectral changes induced in aubergine fruit by chilling injury as influenced by storage time and temperature
The early non-destructive detection of chilling injury (CI) in aubergine fruit was investigated using spectroscopy. CI is a physiological disorder that occurs when the fruit is subjected to temperatures lower than 12 °C. Reference measurements of CI were acquired by visual appearance analysis, measuring electrolyte leakage (EL), mass loss and firmness evaluations which demonstrated that even before three days of storage at 2 °C, the CI process was initiated. An ANOVA-simultaneous component analysis (ASCA) was used to investigate the effect of temperature and storage time on the Fourier transform – near infra-red (FT-NIR) spectral fingerprints. The ASCA model demonstrated that temperature, duration of storage, and their interaction had a significant effect on the spectra. In addition, it was possible to highlight the main variations in the experimental results with reference to the effects of the main factors, and with respect to storage time, to discover any major monotonic trends with time. Partial least squares-discriminant analysis (PLS-DA) was used as a supervised classification method to discriminate between fruit based on chilling and safe temperatures. In this case, only significant spectral wavebands which were significantly influenced by the effect of temperature based on ASCA were utilised. PLS-DA prediction accuracy was 87.4 ± 2.7% as estimated by a repeated double-cross-validation procedure (50 runs) and the significance of the observed discrimination was verified by means of permutation tests. The outcomes of this study indicate a promising potential for near infra-red spectroscopy (NIRS) to provide non-invasive, rapid and reliable detection of CI in aubergine fruit
Probing empirical contact networks by simulation of spreading dynamics
Disease, opinions, ideas, gossip, etc. all spread on social networks. How
these networks are connected (the network structure) influences the dynamics of
the spreading processes. By investigating these relationships one gains
understanding both of the spreading itself and the structure and function of
the contact network. In this chapter, we will summarize the recent literature
using simulation of spreading processes on top of empirical contact data. We
will mostly focus on disease simulations on temporal proximity networks --
networks recording who is close to whom, at what time -- but also cover other
types of networks and spreading processes. We analyze 29 empirical networks to
illustrate the methods
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