A hierarchical dataset of vegetative and reproductive growth in apple tree organs under conventional and non-limited carbon resources

A monitoring of apple fruit, shoot and trunk growth was performed on 15 trees, equally split according to three treatments, which determined heavily contrasting carbon assimilate availability: unmanipulated trees (FRU), thinned trees (THI) and defruited trees (DEF). Several variables describe the vegetative growth on FRU and DEF trees (shoot length, base diameter, number of fruits on shoot, and height, diameter, pruning intensity and number of fruits of the branch carrying the shoot; trunk circumference), as well as the fruit growth on FRU and THI trees (3 fruit diameters). Additional measurements from ancillary shoots (apical diameter, number of leaves, leaf dry weight, stem dry weight, fresh mass, volume) and fruits (3 diameters, dry weight) from trees undergoing the same treatments, provide a more complete (destructive) characterization of organs growth, thanks to several measurements performed across the growing season. Organs are provided with categorical variables indicating the treatment, tree, canopy height, orientation (for both shoots and fruit), as well as branch and shoot identifiers, so that hierarchical modeling of the dataset can be performed. The dataset is completed with dates and day of the year of the measurements and the accumulated growing degree days from full bloom. Data can be used to calculate apple tree absolute and relative growth rates, maximum potential growth rates, as well as shoot growth responses to thinning and pruning. The dataset can also be used to calibrate allometric relationships, estimate structural apple tree growth parameters and their variabilit

Functional group analysis by H NMR/chemical derivatization for the characterization of organic aerosol from the SMOCC field campaign

Water soluble organic compounds (WSOC) in aerosol samples collected in the Amazon Basin in a period encompassing the middle/late dry season and the beginning of the wet season, were investigated by H NMR spectroscopy. HiVol filter samples (PM2.5 and PM>2.5) and size-segregated samples from multistage impactor were subjected to H NMR characterization. The H NMR methodology, recently developed for the analysis of organic aerosol samples, has been improved by exploiting chemical methylation of carboxylic groups with diazomethane, which allows the direct determination of the carboxylic acid content of WSOC. The content of carboxylic carbons for the different periods and sizes ranged from 12% to 20% of total measured carbon depending on the season and aerosol size, with higher contents for the fine particles in the transition and wet periods with respect to the dry period. A comprehensive picture is presented of WSOC functional groups in aerosol samples representative of the biomass burning period, as well as of transition and semi-clean atmospheric conditions. A difference in composition between fine (PM2.5) and coarse (PM>2.5) size fractions emerged from the NMR data, the former showing higher alkylic content, the latter being largely dominated by R-O-H (or R-O-R') functional groups. Very small particles (<0.14 &mu;m), however, present higher alkyl-chain content and less oxygenated carbons than larger fine particles (0.42&ndash;1.2 &mu;m). More limited variations were found between the average compositions in the different periods of the campaign

Online Motion Planning for Safe Human–Robot Cooperation Using B-Splines and Hidden Markov Models

When humans and robots work together, ensuring safe cooperation must be a priority. This research aims to develop a novel real-time planning algorithm that can handle unpredictable human movements by both slowing down task execution and modifying the robot’s path based on the proximity of the human operator. To achieve this, an efficient method for updating the robot’s motion is developed using a two-fold control approach that combines B-splines and hidden Markov models. This allows the algorithm to adapt to a changing environment and avoid collisions. The proposed framework is thus validated using the Franka Emika Panda robot in a simple start–goal task. Our algorithm successfully avoids collision with the moving hand of an operator monitored by a fixed camera

Validation of landslide hazard assessment by means of GPS monitoring technique ? a case study in the Dolomites (Eastern Alps, Italy)

International audienceIn the last years a research project aimed at the assessment of the landslide hazard and susceptibility in the high Cordevole river basin (Eastern Dolomites, Italy) have been carried out. The hazard map was made adopting the Swiss Confederation semi-deterministic approach that takes into account parameters such as velocity, geometry and frequency of landslides. Usually these parameters are collected by means of geological and morphological surveys, historical archive researches, aerophotogrammetric analysis etc. In this framework however the dynamics of an instable slope can be difficult to determine. This work aims at illustrating some progress in landslide hazard assessment using a modified version of the Swiss Confederation semi-deterministic approach in which the values of some parameters have been refined in order to accomplish more reliable results in hazard assessment. A validation of the accuracy of these new values, using GPS and inclinometric measurements, has been carried out on a test site located inside the high Cordevole river basin

XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V Based IoT End Nodes

Heavily quantized fixed-point arithmetic is becoming a common approach to deploy Convolutional Neural Networks (CNNs) on limited-memory low-power IoT end-nodes. However, this trend is narrowed by the lack of support for low-bitwidth in the arithmetic units of state-of-the-art embedded Microcontrollers (MCUs). This work proposes a multi-precision arithmetic unit fully integrated into a RISC-V processor at the micro-architectural and ISA level to boost the efficiency of heavily Quantized Neural Network (QNN) inference on microcontroller-class cores. By extending the ISA with nibble (4-bit) and crumb (2-bit) SIMD instructions, we show near-linear speedup with respect to higher precision integer computation on the key kernels for QNN computation. Also, we propose a custom execution paradigm for SIMD sum-of-dot-product operations, which consists of fusing a dot product with a load operation, with an up to 1.64 × peak MAC/cycle improvement compared to a standard execution scenario. To further push the efficiency, we integrate the RISC-V extended core in a parallel cluster of 8 processors, with near-linear improvement with respect to a single core architecture. To evaluate the proposed extensions, we fully implement the cluster of processors in GF22FDX technology. QNN convolution kernels on a parallel cluster implementing the proposed extension run 6 × and 8 × faster when considering 4- and 2-bit data operands, respectively, compared to a baseline processing cluster only supporting 8-bit SIMD instructions. With a peak of 2.22 TOPs/s/W, the proposed solution achieves efficiency levels comparable with dedicated DNN inference accelerators and up to three orders of magnitude better than state-of-the-art ARM Cortex-M based microcontroller systems such as the low-end STM32L4 MCU and the high-end STM32H7 MCU

Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers

The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit modern architectures in the best possible way. In the case of low-power, parallel embedded architectures, this means finding the configuration, for instance in terms of the number of cores, leading to minimum energy consumption. Depending on the kernel to be executed, the energy optimal scaling configuration is not trivial. While recent work has focused on general-purpose systems to learn and predict the best execution target in terms of the execution time of a snippet of code or kernel (e.g. offload OpenCL kernel on multicore CPU or GPU), in this work we focus on static compile-time features to assess if they can be successfully used to predict the minimum energy configuration on PULP, an ultra-low-power architecture featuring an on-chip cluster of RISC-V processors. Experiments show that using machine learning models on the source code to select the best energy scaling configuration automatically is viable and has the potential to be used in the context of automatic system configuration for energy minimisation

A mixed-precision RISC-V processor for extreme-edge DNN inference

Low bit-width Quantized Neural Networks (QNNs) enable deployment of complex machine learning models on constrained devices such as microcontrollers (MCUs) by reducing their memory footprint. Fine-grained asymmetric quantization (i.e., different bit-widths assigned to weights and activations on a tensor-by-tensor basis) is a particularly interesting scheme to maximize accuracy under a tight memory constraint. However, the lack of sub-byte instruction set architecture (ISA) support in SoA microprocessors makes it hard to fully exploit this extreme quantization paradigm in embedded MCUs. Support for sub-byte and asymmetric QNNs would require many precision formats and an exorbitant amount of opcode space. In this work, we attack this problem with status-based SIMD instructions: rather than encoding precision explicitly, each operand's precision is set dynamically in a core status register. We propose a novel RISC-V ISA core MPIC (Mixed Precision Inference Core) based on the open-source RI5CY core. Our approach enables full support for mixed-precision QNN inference with 292 different combinations of operands at 16-, 8-, 4-and 2-bit precision, without adding any extra opcode or increasing the complexity of the decode stage. Our results show that MPIC improves both performance and energy efficiency by a factor of 1.1-4.9x when compared to software-based mixed-precision on RI5CY; with respect to commercially available Cortex-M4 and M7 microcontrollers, it delivers 3.6-11.7x better performance and 41-155x higher efficiency

Sporadic human prion diseases: molecular insights and diagnosis

Human prion diseases can be sporadic, inherited, or acquired by infection. Distinct clinical and pathological characteristics separate sporadic diseases into three phenotypes: Creutzfeldt-Jakob disease (CJD), fatal insomnia, and variably protease-sensitive prionopathy. CJD accounts for more than 90% of all cases of sporadic prion disease; it is commonly categorised into five subtypes that can be distinguished according to leading clinical signs, histological lesions, and molecular traits of the pathogenic prion protein. Three subtypes affect prominently cognitive functions whereas the other two impair cerebellar motor activities. An accurate and timely diagnosis depends on careful clinical examination and early performance and interpretation of diagnostic tests, including electroencephalography, quantitative assessment of the surrogate markers 14-3-3, tau, and of the prion protein in the CSF, and neuroimaging. The reliability of CSF tests is improved when these tests are interpreted alongside neuroimaging data