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

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

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

Treatment of Pyonephritis Complicated by Septic Shock Using Extracorporeal Device Polymyxin B-Hemoperfusion

Direct hemoperfusion using polymyxin B-immobilized fiber (PMX-DHP) is an established treatment method for septic shock caused by Gram-negative infections. We report one instance in which PMX-DHP therapy has been used successfully in a 33-year-old woman with septic shock from urosepsis. Although there is lack of recommendations in latest Surviving Sepsis Campaign Guidelines, evidence of PMX-DHP efficacy in this subset of patients is growing

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

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.)

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

Climate change 2014 : impacts, adaptation, and vulnerability

Current and future climate-related drivers of risk for small islands during the 21st century include sea level rise (SLR), tropical and extratropical cyclones, increasing air and sea surface temperatures, and changing rainfall patterns (high confidence; robust evidence, high agreement). Current impacts associated with these changes confirm findings reported on small islands from the Fourth Assessment Report (AR4) and previous IPCC assessments. The future risks associated with these drivers include loss of adaptive capacity and ecosystem services critical to lives and livelihoods in small islands.peer-reviewe

Narrative Medicine to integrate patients’, caregivers’ and clinicians’ migraine experiences: the DRONE multicentre project

Background: Although migraine is widespread and disabling, stigmatisation and poor awareness of the condition still represent barriers to effective care; furthermore, research on migraine individual and social impact must be enhanced to unveil neglected issues, such as caregiving burden. The project investigated the migraine illness experience through Narrative Medicine (NM) to understand daily life, needs and personal resources of migraneurs, their caregivers and clinicians, and to provide insights for clinical practice. Methods: The project involved 13 Italian headache centres and targeted migraneurs, their caregivers and migraine specialists at these centres. Written narratives, composed by a sociodemographic survey and illness plot or parallel chart, were collected through the project’s webpage. Illness plots and parallel charts employed open words to encourage participants’ expression. Narratives were analysed through Nvivo software, interpretive coding and NM classifications. Results: One hundred and seven narratives were collected from patients and 26 from caregivers, as well as 45 parallel charts from clinicians. The analysis revealed migraine perception in social, domestic and work life within the care pathway evolution and a bond between chaos narratives and day loss due to migraine; furthermore, narratives suggested the extent of the caregiving burden and a risk of underestimation of migraine burden in patients’ and caregivers’ life. Conclusion: The project represents the first investigation on migraine illness experience through NM simultaneously considering migraneurs’, caregivers’ and clinicians’ perspectives. Comparing narratives and parallel charts allowed to obtain suggestions for clinical practice, while NM emerged as able to foster the pursuing of migraine knowledge and awareness

Economic evaluation of the treatment of Acute Bacterial Skin and Skin Structure Infections (ABSSSIs) from the national payer perspective : introduction of a new treatment to the patient journey. A simulation of three European countries

The aim of this study was to develop a spending predictor model to evaluate the direct costs associated with the management of ABSSSIs from the National health-care provider's perspective of Italy, Romania, and Spain. METHODOLOGY: A decision-analytic model was developed to evaluate the diagnostic and clinical pathways of hospitalized ABSSSI patients based on scientific guidelines and real-world data. A Standard of Care (SoC) scenario was compared with a dalbavancin scenario in which the patients could be discharged early. The epidemiological and cost parameters were extrapolated from national administrative databases (i.e., hospital information system). A probabilistic sensitivity analysis (PSA) and one-way sensitivity analysis (OWA) were performed. RESULTS: Overall, the model estimated an average annual number of patients with ABSSSIs of approximately 50,000 in Italy, Spain, and Romania. On average, the introduction of dalbavancin reduced the length of stay by 3.3 days per ABSSSI patient. From an economic perspective, dalbavancin did not incur any additional cost from the National Healthcare perspective, and the results were consistent among the countries. The PSA and OWA demonstrated the robustness of these results. CONCLUSION: This model represents a useful tool for policymakers by providing information regarding the economic and organizational consequences of an early discharge approach in ABSSSI management